dataesr/scientific-paragraphs-categorization huggingface dataset:
Taken together, these observations might reflect the clinician choice to offer WW to old and comorbid patients, who therefore would be at a higher risk of symptomatic progression and would be considered for palliative approaches. By contrast, younger men without comorbidities would typically be included in AS programs. These individuals could be considered for curative-intent treatments at the time of disease progression. Our findings demonstrate for the first time that PIONEER offers a unique opportunity to systematically assess the status of real-world evidence in PCa and that this large project can answer specific research questions prioritized by key stakeholders. Indeed, this represented the first attempt in using large-scale real-world data to gain insight into a clinically important question on the management of PCa.
Clinicians, patients, data scientists, and researchers converted data to the Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM), ensuring consistent representation of clinical terms across multiple coding systems and allowing for conducting analyses through a federated model. The study group regularly met every week after the study-a-thon to finalize the study protocol, which was then released on the May 28, 2021 [16]. Prior to the analysis, all data partners obtained institutional review board or equivalent governance approval. Reproducibility between data partners was ensured by the development of a core analytical package used uniformly across datasets. Only aggregated, population-level (ie, not patient-level) results from each data source were shared publicly, and all data partners consented to the external sharing of the result set on data.
Material and methods As previously described for oligometastatic prostate cancer patients  [14] , this is a retrospective, observational, multi-institutional study (Nice, Lille, Monaco), collecting databases of patients with omCRPC who underwent SBRT. Consent of each patient was obtained prior to analysis, after providing clear and fair information on the use of the data. In accordance with current legislation, data collection was registered at the National Health Data Hub under the number N • F20210402112942.
Sampling Rats were collected during spring 2020, spring and autumn 2021 and spring 2022 in the urban park of "la Tête d'Or" in Lyon city (Figure  1 ). The capture of animals was carried out in various types of habitats (wooded areas, buildings, zoological park, hay storage yards, riverside vegetation, restaurants, kids' playgrounds, botanical garden and landfills), using live traps baited with sunflower seeds, carrots and sardine. Traps were set in places where animals had been previously detected (Figure  1 ), and they were removed after 10 or 11 nights. Each trap was geolocated. The traps were checked daily early in the morning. Animal dissections and measurements were performed according to the protocols described in  (19). Capture data were registered in the small mammal database (BPM, http://bpm-cbgp.
Library preparation and sequencing The lung samples were homogenized, and the RNA was extracted using Trizol. Ribosomal RNA was removed using NEBNext rRNA Depletion Kit (Human/Mouse/Rat), and the sequencing libraries were prepared using NEBNext Ultra II RNA library prep kit according to the manufacturer's instructions. The samples were sequenced using NovaSeq pipeline (25) using fastp for trimming adapters and low-quality bases, MEGAHIT v. 1. 2. 8 for de novo assembly and MetaGeneAnnotator for the detection of gene-like regions. These were translated to amino acids with BioPerl and queried against the UniProtKB database using SANSparallel. This was followed by re-assembly against the de-novo assembled consensus sequences using BWA-MEM algorithm implemented in HaVoC pipeline  (26).
Phylogenetic analyses Phylogenetic analyses were performed on three datasets composed of complete (or semi-complete) coding regions of S, M and L segment sequences of SEOV recovered in this study and from sequences from different geographical areas available in Genbank. Sequences of Hantaan, Dobrava and Anjozorobe hantaviruses were used as outgroups in all analyses. Multiple sequence alignments were generated with the Clustal Omega alignment program implemented in SeaView v4. 6. 1. Phylogenetic analyses were then performed using the Maximum Likelihood method implemented in PhyML 3. 0 (available at http://www. atgcmontpellier. fr/phyml/) with a statistical approximate likelihood ratio test (aLRT) for branch support. The optimal substitution models were identified as the GTR+R model for all datasets using the "Automatic model selection by SMS" option implemented in PhyML.
Search strategy and eligibility criteria Databases that maximise veterinary journal coverage  (Grindlay et al. , 2012)   Shojania et al. (2007)  the search was updated on 15 November, 2022. No language or geographical restrictions were applied. Results were exported to EndNote (EndNote X9, Clarivate Analytics) and duplicates were removed. Randomised controlled trials (RCTs) of dogs with acute diarrhoea (<7 days duration) irrespective of aetiology and setting that included the desired outcomes were eligible for inclusion. Trials that included treatments of animals that involved multiple interventions in one treatment arm or comparators were excluded. For PICO 3, studies with other comparators were included if information about treatment duration was available. Pharmaceutical manufacturers of amoxicillin-clavulanic acid and metronidazole were contacted to obtain unpublished studies.
Study selection and data collection Three reviewers (CP, KS, MW) independently screened titles and abstracts in an online tool for systematic reviews  (Rayyan, 2023; Ouzzani et al. , 2016) , as well as evaluating full texts, against the eligibility criteria mentioned above. Information about country, year, publication type, study design, population and baseline characteristics, intervention (substance, dosage, frequency) and comparator, assessment methodology, and exclusion and inclusion criteria, were extracted independently by the same reviewers into a data management tool  (Excel, Microsoft). Outcome data on duration of diarrhoea, mortality, progression of disease, duration of hospitalization, and adverse effects (clinical and non-clinical) were also extracted from all studies. Study authors were contacted by email when data were missing.
, 2023). I 2 values exceeding 60% were indicative of substantial heterogeneity and investigated further  (Guyatt et al. , 2011). Forest plots were used to visually present the results of individual studies alongside the overall pooled estimate. The robustness of results was evaluated by excluding outlying studies from each forest plot in a sensitivity analysis. For data that could not be pooled, a descriptive analysis was provided. Where direct evidence could not be synthesised, a network metaanalysis (NMA) approach was employed. For treatments not directly compared in available RCTs, NMA can provide indirect estimates using a common comparator, and can combine direct and indirect evidence for all available treatment comparisons. The netmeta package in R was used to conduct a random-effects NMA using the frequentist approach.
Quality assessment Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was used to evaluate the quality of the evidence  (Guyatt et al. , 2008). Quality of evidence will hereafter be referred to as "certainty of evidence" in accordance with GRADE terminology  (Guyatt et al. , 2008). The RevMan files were exported to GRADEpro software  (GRADE, 2023)  to produce summary of findings tables that included anticipated absolute effects for dichotomous outcomes (Supplementary file 4). An interpretation of certainty ratings and a description of the domains that were used to evaluate the evidence is presented in Table  2. The GRADE domains were assessed by KS, MW, and LRJ for all individual subgroups (P1-P6) and outcomes. A contextualised approach was used to assess imprecision and inconsistency domains  (Schünemann et al.
Effect of probiotic/synbiotic/prebiotic treatment versus no treatment Duration of diarrhoea was the main outcome for all PICOs, which was assessed by dog owners by predefined unvalidated scoring systems. However,  Herstad et al. (2010)  did not report a scale, only presence or absence of diarrhoea (Table  7 ). Gomez-Gallego et al. (  2016 ) described the mean faecal score after 7 and 28 days, but supplementary data were provided by the authors that enabled calculation of mean duration of diarrhoea. The four probiotic trials were included in a pairwise meta-analysis (Fig. 9 ) that showed a trivial clinical effect in favour of the intervention (MD -0. 68 days; 95% CI -1. 28, -0. 09; I 2 = 0). The single synbiotic trial also showed a trivial effect in favour of the intervention (MD -0. 62 days; 95% CI -1.
Acknowledgement Preliminary results were presented as an Abstract at the 32nd European Congress of Veterinary Internal Medicine for Companion Animals, Gothenburg, 1-3 September 2022. The authors wish to thank  Luigia Scudeller ,  Benedikt Huttner ,  Leonard Leibovici , and  Mical Paul  for providing training in conducting systematic reviews using the GRADE methodology, and  Mats Wänlund  for facilitating KS guideline methodologist certification.
Outcome Data The primary outcome in the SCM was age-standardized GC (International Classification of Diseases 10th code C16) mortality among people aged !40 years. Our secondary outcome was age-standardized mortality rates of other UGI diseases, including esophageal cancer (C15) and peptic ulcer (K25-K27), which might be detected during GC screening. We did not include gastritis and duodenitis (K29) because mortality data were unavailable before 1994. We combined mortality rates of esophageal cancer (C15) and peptic ulcer (K25-K27) because deaths from these causes are rare and therefore susceptible to random fluctuations. 20  We extracted age-specific death rates from the World Health Organization's (WHO) mortality database  28  and then calculated age-standardized rates using the age structure from the WHO standard population.
South Korea and Japan's GC mortality records were available from 1985 to 2019 and from 1950 to 2019, respectively, although the most recent years of data were missing for many other countries. Covariate data were also not available for many counties in earlier years. Thus, to balance the preintervention period length and the number of countries to include as potential control countries (donor pools), we determined our study period as 1985 to 2017 for South Korea and 1965 to 2017 for Japan. Accordingly, w20 preintervention years of data were available for both countries.
Funding  Dianqin  Sun has been supported by the  China Scholarship Council. Duco Mülder ,  Weiran Han , and  Iris Lansdorp-Vogelaar  have been supported by  National Institutes of Health, National Cancer Institute  grant  U01CA265729. James O 'Mahony is funded by  Ireland's Health Research Board  under grant  EIA-2017-054. The funders had no role in the design, implementation, or interpretation of results of this study.
It has important applications to diagnostics, cancer immunotherapy, and vaccination, including the engineering of TCR against target antigens  [3] , or the design of optimized antigens in personalized cancer vaccines  [4]. Given the difficulty to predict the structure and binding interface of pMHC-TCR pairs, predicting their binding affinity from general rules of protein interactions remains a promising but arduous approach  [5, 6]. Recent experimental advances  [7, 8]  have allowed for the generation of an increasing amount of data linking TCR sequences to peptide-MHC (pMHC) complexes, providing a large number of binding pairs. These data are gath-ered in several freely available databases: VDJdb  [9] , IEDB  [10] , McPAS-TCR  [11]. However, the number of possible 7-16 amino-acid peptides is very large, and the potential number of possible TCRs even larger (> 10 60  [12] ), meaning that experiments may only assay a small fraction of possible pairs.
This may introduce a bias as the model can be learning to recognize some specific signal coming from the method used to generate the negative examples. Created with BioRender. com contained in incomplete datasets. Another limitation of existing approaches is that they treat the binding prediction as a supervised learning task, which requires both positive and negative examples to train a binary classifier. However, the biological data at our disposal is not of this type, consisting only of positive examples. To address this issue, negative examples are often generated using random association, but these can lead to subtle biases  [22]. The fraction of random pMHC-TCR functional associations is estimated to be ≈ 10 -6 -10 -4  [25] , meaning that non-binding pairs widely outnumber binding ones.
B. Predicting new TCRs binding to known epitopes We first evaluate the performance of TULIP for epitopes presented on the common HLA-A*02:01 allele, which is commonly used to assess such models  [14]. We compare TULIP with NetTCR-2. 0, a state-of-the-art supervised model  [14]. We collected data for which the epitope, alpha chain, and beta chain were all present. We then created a random split of 85% for training and 15% for testing, excluding any sequences from training in which the TCR was also present in the test set. Negative examples were generated within each split by randomly pairing TCRs to a different epitope, and this process was repeated five times for each sequence. To avoid overlap between the training and test sets, negative examples were sampled within each split. We refer to this database, comprising both the training and test sets, as the Specialized Dataset (SD).
NetTCR was trained on the training SD and its performance was evaluated on the testing SD for each epitope separately using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve as a performance metric. The primary aim of TULIP is to be trainable on a larger database. To ensure a fair comparison, we trained it using the following protocol: Firstly, we removed all sequences from the full database that shared the same alpha or beta chain as the test set of the SD. We trained the TULIP on this filtered dataset for 300 epochs and then fine-tuned it for an additional 300 epochs using the positive examples of the training SD. To compute the AUC, we approximated the probability of binding as log(p(e|α, β, MHC)) -log(p(e|MHC)) (see IV C), which quantifies the increase in the odds of observing e upon being recognized by the TCR.
To illustrate this bias, we implemented a different sampling approach for negative examples within our specialized datasets. Instead of uniformly sampling non-binding TCRs, we uniformly sampled another epitope and then selected one of its associated TCRs. This alternative sampling procedure aims to counteract the bias introduced by the over-representation of TCRs from the most commonly observed epitope in the negative sets, which leads supervised methods to learn the features of TCRs binding to that epitope, instead of learning the features of the positive TCRs. Fig. 2D  shows that performances of both TULIP and NetTCR-2. 0 decrease when this alternative sampling is applied, demonstrating the importance of this bias. This alternative sampling does not affect the TULIP model itself, whose training does not involve negative examples, but it does affect its AUC which relies on negative examples.
C. Generalization to unknown epitopes A major challenge of pMHC-TCR binding models is to be able to generalize, i. e. to make binding predictions on epitopes that were not used in the training set (unseen epitopes). This ability varies a lot depending on the considered epitope, notably as a function of how similar it is to other epitopes used during training, making comparisons between methods and different contexts difficult. Here, we propose a systematic approach for assessing generalization across thousands of unseen epitopes, by stratifying them according to their distance to the training set. We split the full database into a test set comprised of epitopes with fewer than 20 examples, and a training set composed of those with more than 20 examples. The TULIP Transformer was subsequently trained on this training data for 300 epochs, following which its performance was evaluated on the testing set, yielding thousands of AUCs.
For instance, the performance of PanPep on unseen epitopes, which was originally assessed using the HRS method, drops to chance level when using the more stringent UURA (Fig. 3C ). By contrast, TULIP, when tested on the same dataset (and re-trained on data that excluded that test set) retains some predictability. Similar results for DLpTCR are reported in the SI Fig S2A. We also compared our findings with ERGO2, which was trained using the UCRA method for negative sampling. Conducting a test by resampling the TCRs with the more conservative UURA shows that the resulting AUC also decays to values close to chance level (Fig  S2B ). Note that STAPLER  [22]  was also evaluated using UCRA, potentially inflating its performance on unseen epitopes. These findings underscore the risks of using negative samples during training.
E. Repertoire mining for p53 neoantigen recognition The p53 gene plays a crucial role in a wide variety of cancers  [36]. Some of its mutants in tumors give rise to public neoepitopes, such as the HLA A-02*01-restricted epitope R175H (peptide HMTEVVRHC), which are shared across different patients and trigger a reaction from the immune system  [37, 38]. A key challenge in drug and therapy design is to be able to identify the TCRs of the immune repertoire that are specific to that neoepitope and drive the immune response. To assess the ability of TULIP to identify such TCRs, we took a healthy repertoire dataset from  [34] , and supplemented it with five known TCRs specific to HMTEVVRHC  [37, 38]. We first retrained our model using a restricted dataset from which all HMTEVVRHC were excluded. We then computed a binding score between each TCR of the repertoire and the epitope, and ranked TCRs from best to worse.
The rank of the first true epitope-specific TCR is shown in Fig. 4B , and compared to a random ranking. When the analyzed repertoire is small (< 100), the best-ranked TCR is almost always a correct epitope binder. For larger sizes, the rank is improved by a factor 20, greatly reducing the number of sequences to be tested for specificity in further validation. This demonstrates our model's ability to extract predictive lists of TCRs with putative epitope specificity from large and diverse repertoire datasets. Furthermore, to simulate scenarios where a few examples were already known, we introduced four TCRs into the training set and included the remaining TCR in the repertoire. This process was repeated five times for each of the five known TCRs. Surprisingly, in our simulations, having only four known TCRs consistently resulted in the top-ranked or second-ranked position for the sole binding TCR (see Fig.
We emphasize the importance of utilizing all available data sources, regardless of their completeness or quality. The TCR-epitope binding prediction task often suffers from the scarcity of comprehensive data, as obtaining complete TCR sequences along with corresponding epitopes and MHC information is challenging. However, our approach is designed to be flexible, leveraging the available data and accommodating situations where only partial data is accessible. By using both alpha and beta chains when available, while being able to learn from one chain alone, our model can make the most of the data at hand and extract valuable insights. While our proposed model shows promise, it is essential to conduct fair and rigorous model comparisons to assess its performance accurately. The field of TCR-epitope binding prediction often lacks standardized benchmark datasets and evaluation protocols (but see  [13] ), leading to difficulties in comparing different models.
To address this challenge, future research should focus on establishing standardized benchmarks and evaluation procedures that encompass diverse datasets and evaluation metrics beyond classification. One limitation of our approach is that the model yields only probabilities of pairs of sequences, rather than a proper binding constant, for which titration data (where the concentration of the epitope is varied) would be needed. Another limitation is that large areas of the epitope space have not been measured, and some parts are extremely hard to measure. For example, having a model able to determine the risk that a TCR binds to self-proteins, would be extremely useful for predicting the safety of T-cell therapy, but such TCRs are by construction hard to observe, and the lack of data is a major limitation for further progress in this direction.
A. Data collection Data acquisition in the field of immunology presents a major challenge. The intricate process of T cell receptor (TCR) binding to its respective epitope depends on four critical elements: the epitope itself, the major histocompatibility complex (MHC), and the alpha and beta chains of the TCR. While each component has been extensively studied in isolation, the number of instances where all four components are jointly available remains remarkably scarce. In this study, we present a novel computational aiming approach at constructing a model capable of from incomplete data. To achieve this goal, we curated data from multiple sources, maximizing the total sample size at our disposal. Specifically, we first accessed the VDJdb database  [9]  in its entirety, which boasts the highest data quality among our available resources (see Table  I ).
We also added the IEDB database of Tcell receptors and McPAS-TCR dataset  [10, 11]  (see Table  I ). The IEDB database is more diverse but we observed a much poorer quality of the data, and there was never used for finetuning. This accounts for 209779 not redundant data points containing the epitope and at least one chain of the TCR. The instances listed above consist of T-cell receptors along with their respective epitopes and major histocompatibility complex (MHC). Regrettably, the MHC or one of its two chains is frequently absent. Additionally, the diversity of epitopes is relatively low compared to that of TCRs, with each epitope possessing multiple T-cell receptors. More statistics on the repartition can be found in the Supplementary Material. To supplement our data, we incorporated the training database of netMHC, which is solely composed of MHC and epitope information.
Although this does not directly aid in comprehending the correlation between TCR and epitope, it is advantageous in two ways. Firstly, the dataset encompasses a wide range of epitopes, which assists the model in comprehending the true diversity of potential epitopes. Secondly, in order to achieve effective transfer learning between MHC, the model must comprehend what is distinct to each MHC and what can be transferred. Therefore, the netMHC database aids in better modeling the specific role of MHC in the epitope modeling process. We gather 663767 peptide with their MHC (see Table  I ). We gathered all this data in a single one that we will refer to as the Full Dataset (FD).
5 in this paper, and sum over the sequence in the training set: Loss(θ) = x∈train (1 -λ)Loss MLM (x) + λLoss CLM (x)  (4)  where x = (x α , x β , x e , MHC) is our raw datapoint and θ are the parameters of the model. Details on the training of a transformer can be found in appendix. Code and weights for the model can be found at https: //github. com/barthelemymp/TULIP-TCR/ C. Mutual information as a proxy to the binding probability The model presented before is autoregressive. The structure of the probabilities defined by the model is simply P (a α i |a α <i , z β , z epitope , M HC) (resp β, epitope) and a simple multiplication over the position gives us a conditional probability on the sequences (p(e|α, β, MHC), p(α|e, β, MHC), p(β|e, α, MHC). However, we should be more precise on what we want to evaluate.