dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , number of individuals observed) provided by observers. In some cases (4% of the observations in our dataset) observers did not provide an abundance and instead reported species presence with an "X". We treated these as NA values in our analyses. Vertical lines represent the limits of the breeding season as we defined it (i. e. , the 2. 5 and 97. 5% circular quantiles; see Table  S1 ). Bar colours show whether plotted data fall within (green) or outside (grey) the breeding season. Table  S1 : Breeding season per latitudinal band. For two tropical bands (-10° to 0° and 0° to 10°), we considered that breeding occurs all year round. For all other bands, beginning and end dates were derived from the temporal distribution of breeding codes in eBird records (Fig. S1 ), as the 2. 5 and 97. 5% circular quantiles of Julian days.
3) Calculating observer calibration index We calculated an individual observer calibration index, closely related to the one calculated in Cazalis et al. , (2020) (following  Kelling et al. , (2015)  and  Johnston et al. , (2018) ), for each observer included in the analyses (i. e. , with more than a given threshold of experience: ≥ 50 checklists for a total of ≥ 100 species in the Americas during the study period). This index was calculated from a subset of the eBird records, selected through a somewhat different filtering process from the one used in the main analyses. As in the main analyses, we kept only checklists that reported all species detected, observations from 2010-2019, and excluding disapproved observations; in contrast, we did not restrict the dataset based on protocol type or sampling effort.
For any given observer, we calculated the observer calibration index as the log-scaled number of species an observer is expected to report on average during a standard sampling event. Usually this is done by running a mixed-effects model with the richness of each checklist as the response variable, with sampling effort and ecological drivers of species richness as explanatory variables, and with observer as a random effect  (Johnston et al. , 2018; Kelling et al. , 2015; Cazalis et al. , 2020). In this study, because of computing limitations, we split this calculation into two models: first we fitted a GAM assuming a negative binomial distribution (using the function bam from the 'mgcv' package  (Wood, 2011) ) with the following structure: species_richness ~ protocol_type + number_observers + s(duration) + s(starting_time) + te(longitude, latitude, Julian_day), with starting_time the time at which the sampling event started, s() a smoothed term enabling complex relation between variables, and te() an interacting smooth term enabling here richness to vary across space and time.
We then extracted residuals of this model and used them in a linear mixed-model (using the function lme from the 'nlme' package (Pinheiro et al. , 2020)) with no explanatory variable and a random effect on observer. Finally, we simulated a sampling event under the "Stationary Points" protocol, fixing number of observers, duration, starting time, longitude, latitude and Julian day to their median values. We predicted the log-scaled species richness that should be detected in this hypothetical sampling event according to the GAM, and summed it with the random effect of each observer in order to get an observer calibration index per individual observer.
4) Bird species traits BirdBase is a regularly updated global database of the ecology and life history traits of the world's bird species  (Şekercioğlu et al. , 2004, 2019). We extracted from it the species' primary habitat, structured into 10 classes (after exclusion of 'coastal' and 'sea' species): Artificial, Deserts, Forests, Grasslands, Riparian, Rocky, Savannahs, Shrublands, Wetlands, Woodlands. Primary diet consisted of eight classes (after combining 'Carnivore', 'Scavenger' and 'Vertebrate' into 'Carnivorous'; combining 'Plant' and 'Herbivore' into 'Herbivorous'; and considering the 42 species with an 'unknown' diet as 'Insectivorous'): Carnivorous, Frugivorous, Granivorous, Herbivorous, Insectivorous, Nectarivorous, Omnivorous, Piscivorous. In addition, we obtained from the same database: migratory status ('strict', 'partial', or 'sedentary'); body mass; and species taxonomic Order (e.
g. , Accipitriformes, Anseriformes). We calculated a specialisation index for each species based on the number of different habitat (HB) and diet (DB) categories for each species, as in  Şekercioğlu (2011) : log [100/(HB x DB)]. We inferred specialisation and body mass values of species for which it was unknown (respectively N=43 and N=358), by using the mean specialisation and mass of the documented species in the same taxonomic Family. We extracted Red List status from BirdLife International (2019), and (following  Butchart et al. , 2007)  transformed it into a quantitative variable (from Least Concern as 1, to Critically Endangered as 5). We treated the six species for which the Red List status was Data Deficient as LC. We calculated each species' breeding range size (as defined above) from BirdLife International and HBW (2019), within our study area.
All spatial data analysed are open access and free to use for research. Bird abundance data were taken from eBird, version of October 2019 (accessed on the 30 th of November 2019) and are available on request at http://www. ebird. org. Species distribution maps came from BirdLife International and HBW version 2019. 1 and are available on request at http://datazone. birdlife. orghttp://doi. org/10. 5281/zenodo. 5004010.
Features impacting prediction confidence As shown in Fig. 1a , protein pairs present in the Protein Data Bank (PDB) are enriched in high-scoring models compared with pairs in HuRI and Hu. MAP. There could exist several possible explanations for this, such as the inability of AlphaFold2 to identify transient or indirect interactions. Nevertheless, it is also possible that the two high-throughput datasets contain noninteracting pairs. Therefore, to understand this difference better, we first studied an additional dataset created from large (>10 chains) heteromeric protein complexes. The set of large complexes consists of 12 large heteromeric protein complexes, and all (nonidentical) pairs of protein chains in each complex were docked with each other. These pairs can be divided into the ones with direct interaction and those that do not interact directly.
Here, we used a definition of more than 20 contacts of less than 8 Å between Calphas to exclude small interaction interfaces. When a complex contained multiple copies of identical chains, all interactions were included to allow for alternative interactions between the chains. The difference in pDockQ scores between the direct and indirect interacting pairs is striking, where only 6% of the indirect pairs have a pDockQ score > 0. 5 compared with 38% of the directly interacting pairs (Fig. 2a ). This shows that directly interacting pairs often can be predicted even when they are part of large complexes, in contrast to indirectly interacting pairs. hu. MAP has many more high-confidence predictions than HuRI, which is based on Y2H experiments. To further understand this difference, we first analyzed a subset of all protein pairs from the CORUM  23  database, the best manually curated database of mammalian protein complexes, and predicted the interaction of all pairs in the same complex.
The average pDockQ score of CORUM is slightly higher than for hu. MAP, but the number of high-quality predictions is similar (16% versus 19%), indicating that the different databases of protein complexes have a similar fraction of high-confidence predictions and that HuRI is the outlier (Fig. 2b ). It is unlikely that the Y2H in HuRI data should contain a large set of indirect interactions, as only two human proteins are expressed in the same cell. Therefore, there must be another reason for the few high-confidence predictions. We examined the properties of the pairs present in the two datasets. Here, it can be seen that HuRI proteins differ from the hu. MAP (and other datasets) in two ways. HuRI protein pairs contain more intrinsic disorder (Fig. 2c ) and have fewer efficient sequences (meff) in their multiple sequence alignments (MSAs) (Fig.
Structure prediction of human protein interactions We selected experimentally identified human protein interactions from the Human Reference Interactome (HuRI)  2  and the Human Protein Complex Map (hu. MAP v. 2. 0)  3. HuRI comprises protein interactions determined by yeast two-hybrid (Y2H) screening 2 from which we modeled 55,586 pairs. From hu. MAP we selected 10,207 high-quality PPIs  3. While HuRI is more likely to be enriched for direct protein interactions, including transient partners, the hu. MAP set is more likely to reflect stable protein interactions, including members of the same complex that may not be interacting directly. The overlap between the two datasets is small (309 pairs), and a comparison with two large-scale compendiums of structural models  4  indicates that 62,019 of the combined pairs do not have experimental models nor can they be modeled easily by homology, suggesting a large potential gain in structural knowledge.
We predicted the structure of 65,484 nonredundant pairs using the FoldDock pipeline  18  , based on AlphaFold2 (ref. 17 ). As in the FoldDock pipeline, we combined size and the predicted local Distance Difference Test (plDDT) scores of the interface into a single score to predict the DockQ score of a complex, dubbed pDockQ (Methods), which can rank models by confidence. We tested pDockQ score by comparing the predicted models with 1,465 experimental models, of which 742 (50%) were correct (DockQ > 0. 23). For predictions with pDockQ > 0. 23, 70% (671 of 955) are well modeled, and for pDockQ > 0. 5, 80% (521 of 651). We show in Fig. 1a  the distribution of pDockQ for the predicted and random protein interactions, and provide data for all models in Supplementary Table  1. The pDockQ of known interacting proteins tends to be higher than for the random set, with the predictions for hu.
All subunits can be modeled, with the final complex generally agreeing (Fig. 6 ) with a cryoEM structure for these subunits (PDB:6NMI). The most significant difference to the cryoEM model is the relative positioning of the ERCC3 subunit. The exact final model showing the distribution of changes in predicted binding affinity (ΔΔG) for mutations known to have an impact (orange) versus the ones with neutral effect (green). The boxes represent the first and third quartiles. The upper whisker extends from the third quartile to the largest value no further than 1. 5 × interquartile range (IQR). The lower whisker extends from the first quartile to the smallest value at most 1. 5 × IQR. The statistical significance of the differences was tested using two-sided Wilcoxon rank sum test with continuity correction.
Article https://doi. org/10. 1038/s41594-022-00910-8 obtained can vary depending on the aligned pairs, with multiple possible final conformations (Supplementary Fig. 3 ). Figure  6  illustrates the conformation that best matches the cryoEM model in PDB:6NMI. For example, for the TFIIH core complex, there is a predicted model where the complex adopts a more open conformation (as seen in PDB:5OQ J) and alternative predicted placements of the GTF2H1 subunit. The RFC complex is also composed of five subunits with 1-to-1 stoichiometry. One iterative alignment of pairwise protein interactions builds a model that includes all five subunits organized similarly to that observed in the PDB:6VVO cryoEM structure (Fig. 6 ). In this predicted model, the subunits RFC2/5/4/3 match the experimentally observed model well, but there are apparent deviations introduced by compounding errors in alignment by this iterative process.
Next, we add the highest-ranked dimer, which shares one subunit with the complex if it does not overlap; this is repeated for all dimers until the complex is complete or no additional proteins can be added. We tested this on the 20S proteasome, a particularly challenging example, with stoichiometries different from 1-to-1 and homologous subunits. This automatic procedure could build a model containing all 14 subunits (half of the proteasome), which are mostly placed in agreement within the experimental model (Fig. 6 ). However, the exact order of the chains is incorrect, that is, at each location an incorrect protein is placed, highlighting that AF2 cannot distinguish which two proteins interact from a set of homologous proteins. Two additional proteins where we could build a good model are Heterodisulfide reductase from Methanothermococcus thermolithotrophicus (PDB:5ODC) and the eukaryotic translation initiation factor 2B from Schizosaccharomyces pombe (PDB:5B04) (Supplementary Fig.
4 ). For PDB:5ODC we could build a complete model of the protein with an r. m. s. deviation of 6. 0 Å (TM-score 0. 90)  30  starting from dimers. However, for PDB:5B04 it was not possible as the chains started overlapping when we tried to build a larger model. However, if we build trimers and then use all three dimers from these trimers we can build a complete model with an r. m. s. deviation of 7. 3 Å (TM-score 0. 86), showing that it is sometimes necessary to use larger subunits to assemble the complexes. Results from a follow-up study  31  show that it is often possible to build the structures of complexes if the subunits are well predicted. In summary, we find that it is possible to iteratively align structures of pairs of interacting proteins to build larger assemblies, but we also identified issues that limit this procedure at the moment.
Concluding discussion We have predicted complex structures for pairs of human proteins known to physically interact from two different datasets based on different experimental approaches. We note that the source of data used for the protein interactions is important and impacts the fraction of models that can be confidently predicted. Our analysis suggests that protein interactions supported by a combination of affinity-, co-fractionand complementation-based methods result in higher-confidence models. We believe these protein interactions tend to correspond to high-affinity interactions which are very likely to share a direct physical permanent interaction. We show that it is possible to use metrics from the models (for example, pDockQ score) to rank higher-confidence models, providing an additional accuracy level to large-scale PPI studies, and in the future to provide additional high-quality targets for detailed studies of stable complexes.
Acknowledgements R. A. S. acknowledges funding through the  European Union   Horizon 2020 program  INFRAIA project  Epic-XS  (project no. 823839 ) and the research program  NWO TA  with project no. 741. 018. 201 , which is partly financed by the  Dutch Research Council (NWO). A. E. was funded by the  Vetenskapsrådet  (grant no. 2016-03798  and  2021-03979 ) and the  Knut and Alice Wallenberg Foundation. The computations/data handling were enabled by the supercomputing resource Berzelius provided by the  National Supercomputer Centre at Linköping University  and the  Knut and Alice Wallenberg Foundation  and  SNIC , grant nos. SNIC 2021/5-297  and Berzelius-2021-29. J. E. B. acknowledges funding from the  National Heart Lung and Blood Institute  (grant no. 5R35GM13625 ) and the  National Institute for General Medical Sciences  (grant no.
Protein interaction data and annotations Human protein pairs known to physically interact were obtained from the hu. MAP dataset, retaining pairwise protein interactions with ≥0. 5 confidence, and most interactions from the HuRI dataset. These interactions were further enriched by obtaining annotations on crosslinked peptides matched across pairs of interaction proteins, disease-related mutations and protein phosphorylation sites in the selected proteins. In addition, all nonhomologous pairs from 12 protein complexes (Supplementary Table  5 ) and 4,320 protein pairs from 2,102 different protein complexes (Supplementary Table  6 ) in CORUM  23  were used for additional analyses. A complete list of all datasets is available from the supplementary data. 4] [35] [36] [37] [38] [39] [40] [41] [42] [43]  , and filtered for peptides unique to only one protein sequence.
Protein complex prediction To predict protein complexes of pairwise interactions, we used the FoldDock pipeline  18  based on AlphaFold2 (ref. 17 ). We used the option of fused + paired MSAs and ran the model configuration m1-10-1 as this provides the highest success rate accompanied by a 20-fold speed-up. Both the fused and paired MSAs were constructed by running HHblits on every single chain against Uniclust30. The fused MSA was generated by simply concatenating the output of each of the single-chain HHblits runs for two interacting chains. The paired MSA was constructed by combining the top hit for each matching OX identifier between two interacting chains, using the output from the single-chain HHblits runs.
pDockQ confidence score To score models, we used features from the predicted complexes to calculate the predicted DockQ score, pDockQ. This score is defined with the following sigmoidal equation: pDockQ = 0. 707 1 + e -0. 03148(x-388. 06) + 0. 03138 where x = average interface plDDT*log(number of interface contacts). The parameters were optimized to predict the DockQ score using the dataset from ref. 45. The number of interface contacts is defined as elsewhere in this paper (any residues with an interface atom within 10 Å of the other chain), and the plDDT is the predicted lDDT score from AlphaFold2 taken over the interface residues as defined by the interface contacts.
Analysis of phosphosites in the protein-protein interfaces Phosphosite residues in interfaces were identified from a previously published comprehensive list of known human phosphosites  26. Kinases associated with phosphorylation of interface residues were obtained from the PhosphositePlus database, and over-representation analysis of kinases was performed using a hyper-geometric test. Highly regulated interface phosphosites were defined as those with more than twofold change in phosphorylation in more than two perturbation conditions across a collated phosphoproteomics dataset comprising a range of physiological conditions and drug treatments  29. Pearson correlation was calculated amongst these regulated phosphosites and clusters of co-regulated phosphosites were identified using hierarchical clustering ('Ward' method) of Euclidean distances of the correlation matrix.
Phosphosite clusters were created by cutting the dendrogram at the appropriate level using the cutree (h = 17) function in R. Phosphosite clusters that were significantly regulated in each perturbation condition were identified by a Z-test from the comparison of fold changes in phosphosite measurements of all phosphosites in a cluster against the overall distribution of phosphorylation fold changes across the condition. Gene ontology over-representation of each cluster was performed separately using a hyper-geometric test in R. The gene ontology terms were obtained from the c5 category of the Molecular Signature Database (MSigDBv7. 1)  46. All over-representation analyses were performed using the enricher function of the clusterProfiler package (v. 3. 12. 0) 6 in R.
[9] [10] [11] [12] [13]  They often rely on either rule-based algorithms specifically designed with clinicians, or on supervised models trained on annotated patient datasets. Such algorithms are limited because their development is disease specific, must be (re-)started from scratch for every new disease and demand a lot of clinician expertise time. In addition, portability and generalization to new databases (e. g. different hospitals) can often fail, requiring once again the process to be reiterated in the new institution. Hripcsak and Albers defined high-throughput phenotyping as an approach that "should generate thousands of phenotypes with minimal human intervention". [8]  In this perspective, multiple methods have been developed for automatic phenotyping. Agarwal et al. proposed XPRESS which learns on noisy labels.
Yet, we need increased resolution for phenotyping, especially for studying acute diseases (that can occur repeatedly) or for answering epidemiological questions (where temporal sequence is important): phenotyping at the visit level would allow to precisely take into account the dynamic evolution of patient's conditions. Besides, those frameworks were developed using English databases, leveraging advanced NLP tools and relying on rich terminologies not necessarily available in other languages. [15, 16]  Portability to other languages is not straightforward, as they still often lack resources of matching quality. We propose a new, portable, approach for unsupervised algorithm extending PheNorm at the visit level: PheVis. This new PheVis method accumulates past information to provide an up-to-date estimation of a phenotype probability at any given visit.
Input data The input data of the PheVis approach are the clinical notes and the ICD10 codes from an EHR datawarehouse. All the notes and ICD10 codes are collapsed by visit, and IAMsystem, a dictionary-based named entity recognition tool, is used to extract relevant Unified Medical Language System (UMLS) concept unique identifiers (CUIs, i. e. CUIs associated with disease to be phenotyped -see Section 2. 3 for details). [17, 18]  CUIs features are the number of occurrence of the terms in the UMLS dictionary related to the CUIs. No semantic analysis is performed avoiding the distinction between current disease and past disease or negation. ICD10 codes are aggregated at the category level (i. e. the first three characters, M05. 1 and M05. 2 codes are both counted under the same category code M05).
Probability estimation To estimate the disease occurrence probability, we used a noising-denoising logistic regression with random intercept similarly to PheNorm. First,  (10 * , ) visits are randomly sampled with replacement with inverse probability weighting defined as. Then we perform a noising-denoising step to force the algorithm to use other variables than the main ICD and UMLS concepts (and thus avoid overfitting with respect to the surrogate construction). Every value of explanatory variables has a probability of  ;14% = 0. 3 to be replaced by the mean of the explanatory variable as in PheNorm. [13]  For instance if M05 ICD10 code mean occurrence is 0. 2 then each visit has a probability of 0. 3 to have its true M05 value replaced by 0. 2. This noisy matrix of dimension 𝑚𝑎𝑥(10 * , 𝜑) × 𝑃′ is denoted X !" < : X !" < = r 𝑚𝑒𝑎𝑛>𝑋 ; " ?, if 𝑟 ;14% &' = 1 X !" = , if 𝑟 ;14% &' = 0 with 𝑟 ;14% ∼ 𝐵𝑒𝑟𝑛(𝑝 ;14% ) (6) For the denoising step a logistic regression with random intercept is used: 𝑙𝑜𝑔𝑖𝑡 ~𝑃 •𝑃𝐿 ! !" = 1€• = 𝑋 ! !" " % > 𝛽 " + 𝑏 ?# with 𝑏 ?# ~ 𝑁(0, 𝜎 ? @ ) (7) And finally the probability of having the disease is estimated on the noise free matrix as: 𝑃(𝐷𝑖𝑠𝑒𝑎𝑠𝑒 ! = 1) = 𝑒 AB &' ( C ) D ' 1 + 𝑒 EB &' ( F ) D ' (8) This final probability illustrates the level of confidence of the estimated phenotype based on the used variables.
Application results Figure  2  shows individual PheVis and other state-of-the-art methods predictions for four patients. For RA, as the information is accumulated over time in PheVis, the model is able to maintain relatively stable predictions over time even if there is not much information about RA in a visit. Without this feature, other approaches display highly variables predictions oscillating over time. For TB, the advantage of accumulating information confronts problem of accumulating too much. This problem is increased in French because the majority of natural language processing tools and terminological systems were developed for the English language. Tools to detect negation and past history are not yet implemented in Bordeaux University Hospital datawarehouse. [23]  Prediction of PheNorm is really close to 0.