dataesr/scientific-paragraphs-categorization huggingface dataset:
Effector Prediction Since the cowpea aphid genome has not been sequenced, homologous proteins from the different aphid species or those based on cowpea aphid transcriptome, used in our custom database, were used for these analyses. Multiple bioinformatics tools were harnessed to screen the identified salivary proteins for putative effector function. First, the salivary proteins were evaluated for secretion using tools that predict classical and nonclassical secretions, SignalP and SecretomeP  (Bendtsen et al. , 2004a,b; Armenteros et al. , 2019) , respectively. Using SignalP, a secretion signal was detected in 29 (19. 46%) proteins, while SecretomeP predicted the secretion of an additional 23 (15. 44%) of the 149 salivary proteins (Table  1 ). To eliminate proteins with transmembrane domains, presence of transmembrane helices was evaluated using TMHMM  (Sonnhammer et al.
, 1998). Six of these predicted secreted proteins contained transmembrane helices. A machine learning approach was recently used to develop novel prediction program for fungal effectors  (Sperschneider et al. , 2016 (Sperschneider et al. , , 2018)). We wondered whether this tool, EffectorP, could be used to predict aphid effectors. To test this, we first subjected known aphid effectors for EffectorP analysis. We tested the C002 effector, identified first in pea aphid  (Mutti et al. , 2008) , and Me10, identified in potato aphid  (Atamian et al. , 2013). Both C002 and Me10 were identified as effectors by EffectorP indicating that EffectorP can be utilized as a tool to screen for aphid effectors. Using EffectorP, 20/149 (13. 4%) of the cowpea aphid salivary proteins were identified as putative effectors (Table  1 ).
Effector Prediction Here we reported the use of a machine learning plant-pathogenic fungi effector prediction program, EffectorP, for prediction of aphid effectors  (Sperschneider et al. , 2016 (Sperschneider et al. , , 2018)). We confirmed the use of EffectorP as a possible program for identifying aphid effector proteins by successfully subjecting the well-characterized aphid effectors C002 and Me10 to EffectorP analysis  (Mutti et al. , 2008; Atamian et al. , 2013; Pitino and Hogenhout, 2013; Chaudhary et al. , 2019). Interestingly, EffectorP predicted 20/149 of the cowpea aphid proteins as effectors. Among these 20 proteins, is the functionally characterized Me10 effector and three proteins which have been predicted for effector function  (Atamian et al. , 2013; Elzinga et al. , 2014; Thorpe et al.
ACKNOWLEDGMENTS We thank  Dr. Monika Ostaszewska-Bugajska  ( University of Warsaw ) for sharing methylglyoxal evaluation protocol,  Dr. Philip Roberts  ( UC Riverside ) for the cowpea aphids, and  Dr. Jiangman He  ( UC Riverside ) for help with confocal microscopy and constructing the pCAMBIA-1300-GW-mScarlet vector.
To test its practical value, the bubble generator was used to find AS events in a real dataset. As reported in  [1] , this bubble generator was able to achieve about the same precision in identifying AS events as the state-of-art-algorithm KisSplice  [18, 22] , but unfortunately building the bubble generator was about 5 times slower than finding AS events with KisSplice. Despite its great theoretical value, this poses a serious limitation on the practical application of this bubble generator to large-scale datasets, which are typical of biological applications. To address this issue, in this paper we present a new family of bubble generators which improves substantially on the one proposed in  [1]. In particular, in the same RNA dataset used in  [1] , bubble generators in our family are about two orders of magnitude faster in practice than the bubble generator in  [1] , and improve the precision in identifying AS events from 77.
3% to 90%. Furthermore, as the generators are truly fast in practice, we are now able to analyse a larger dataset that was not possible with the bubble generator proposed in  [1]. When compared to the stateof-the-art algorithm for identifying AS events, our bubble generators are also much faster than KisSplice  [18, 22] , have similar precision, and find AS events that KisSplice cannot find. In the experiments, we observed that our new generators also contain many bubbles that correspond to a particular type of AS event, namely intron retention (IR), which is usually considered a hard-tofind event. We believe that our experimental findings make the new bubble generators the method of choice for finding AS events in a reference-free context, especially in large-scale data sets. From the theoretical viewpoint, our new generators are of minimum size (i.
Experimental results To test the usefulness of our family of generators in practice, we applied it to the identification of AS events in RNA data in a reference-free context. In order to compare our generators to both the state-of-art algorithm KisSplice  [18, 22]  and to the generator defined in  [1] , we used two datasets: Dataset 1 is the same dataset as in  [1]  which consists of a sample (corresponding to chromosome 10) of 4,932,572 RNA-seq Illumina paired-end reads extracted from the mouse brain tissue (available in the ENA repository under the following study: PRJEB25574). Dataset 2 corresponds to a sample of 10 million reads from the dataset presented in  [3]  of human lung cells infected by Influenza A viruses (IAVs). We built the de Bruijn graph from these reads and applied standard sequencing-error-removal procedures by using KisSplice  [18, 22].
Similarly to BFS and DFS, also SFS can produce a tree as follows. Initially, the tree is empty. Whenever a vertex v is scanned, all the edges between v and its previously unmarked neighbours are added to the tree. In our experiments, we implemented SFS with a random choice of the next vertex to be scanned, and averaged on 1,000 runs with different random seeds. To compute the source set of the de Bruijn graph, we computed in linear time the DAG of its strongly connected components and chose a vertex from each source. The de Bruijn graph corresponding to our dataset had a total of 83,400 vertices, 99,038 edges and 18,385 source vertices. Finally, we recall that for general graphs, our new generators are not necessarily minimal. In order to avoid producing duplicates of the same bubble, we discarded a bubble whenever its source was already contained in a tree previously computed from another start vertex.
Here we show how the set of bubbles belonging to generators in our family can be used to predict AS events. Notice that our method is reference-free; however, in order to evaluate it, we make use of annotated reference genomes to assess if our predictions are correct. To estimate the precision of our new generators in predicting AS events we proceed as follows. We consider the whole set of bubbles belonging to the generator. We then apply the same filter (based on the length of the legs) as in KisSplice to extract the bubbles that can be considered as putative AS events. To determine the true AS events, we used STAR  [9]  to map the putative bubbles of Dataset 1 to the Mus musculus reference genome and annotations (Ensembl release 94) and those of Dataset 2 to the human genome (hg38, Gencode v36).
We then analysed the results using KisSplice2RefGenome  [4]. Following  [18] , a bubble corresponds to a true AS event (or a true positive (TP)) if one leg matches the inclusion isoform and the other the exclusion isoform. Otherwise, the bubble is classified as a false positive. The precision of the method is defined as T P/(T P + F P ). Below we detail the analysis of the results for the two datasets considered. We compared our results to both SP-Gen and KisSplice. We report that Dataset 2 was already too large to be handled by SP-Gen, which did not finish even after 1 day, whereas KisSplice and the tree generators from our family produced the results in approximately 10 minutes. As a result, we will not report data from SP-Gen on Dataset 2.
Analysis of Dataset 1: comparison with the SP-Gen and KisSplice The results for DFS/BFS/SFS and SP-Gen are reported in Table  2. The results show that the number of true AS events found by our generators is comparable to the number of true AS events found by SP-Gen whereas the number of false positives is significantly smaller. Indeed, our generators have a precision between 87. 7% and 91. 6%, compared to 77. 3% for the SP-Gen. An interesting aspect of SP-Gen was that it contained many bubbles that were classified as Intron Retention (IR), which is a type of AS event that is generally particularly hard to identify. As shown in Table  2 , the number of IR for our generators remains similar to the one found by SP-Gen. Since the computation of generators in our family is truly fast in practice, we combined them by taking the union of bubbles coming from different generators and tested whether this would increase the number of AS events found.
Analysis of Dataset 2: comparison with KisSplice As already mentioned, this dataset was too big to analyse by SP-Gen. Indeed, SP-Gen did not finish even after 1 day, while the generators of our family and KisSplice took approximately 10 minutes to produce the results. As a result, we report here only the comparison of our new tree generators with KisSplice. As reported in Table  4 , our new generators produce results that are comparable to KisSplice. As a matter of fact, our generators produce a number of true AS events that is similar to the one generated by KisSplice and the same holds for IR events. By combining the results of our generators, we get a slightly higher number than KisSplice for both true AS events and IR events. Notice also that both the generator and KisSplice have a low precision on this dataset, meaning that most of the bubbles found in the sample do not correspond to AS events in human.
This is not surprising as this dataset contains many reads coming from the virus  [3]. Finally, the generators produce up to 155 true AS events that are certainly missed by KisSplice as they correspond to bubbles with more than 5 branching vertices. In summary, our new generators allowed us to analyse this dataset, which was not possible with SP-Gen. Although the new generators show a similar performance as KisSplice in terms of both the number of true AS events and computational time, they offer a nice complementary view as they are able to include true AS events that were missed by KisSplice.
A preliminary version V. Acuña is supported by  CMM   ANID PIA FB210005  and  ACE210010 -CNRS IRL #2807  and  Center for Genome Regulation FONDAP 15090007. G. F. Italiano is partially supported by  MIUR , the  Italian Ministry for Education, University and Research , under  PRIN  Project  AHeAD  ( Efficient Algorithms  for HArnessing Networked Data). B. Sinaimeri  and M. -F. Sagot are partially funded by the  French ANR  project  Aster  ( 2016-2020 ). Part of this work was done while G. F. Italiano was visiting  Université de Lyon  and B.
Further, early contributions from the epoch of reionization  [17, 18]  can be used to add an additional constraint on the optical depth at re-ionization τ rei , which would then break the degeneracy between it and the amplitude of the primordial power spectrum A s in CMB measurements  [19]. Both directions will undoubtedly benefit from upcoming high-resolution CMB experiments and large-volume galaxy redshift surveys. Indeed, the advent of CMB-S4  [20]  should allow for the novel application of kSZ tomography, i. e. measurements of kSZ signal at different redshifts. The result of which could then be cross-correlated with datasets from DESI  [21]  or LSST  [22]  to constrain either primordial, local non-Gaussianity  [23]  or the evolution of ionized gas  [24]. This wealth of scientific return from kSZ measurements has motivated several attempts to detect this effect using various datasets and estimators, despite the fact that the kSZ signal is deeply buried beneath the primary CMB anisotropies.
g. 34, 35]. We apply our method on the Planck SMICA2018 CMB map  [36]  and the maxBCG cluster catalog  [37]. Our ensemble of velocity reconstruction is obtained from the Bayesian forward modeling of galaxy clustering using LOWZ and CMASS galaxy samples presented in  [38]. For consistency, except for the estimation of the CMB contribution to the covariance matrix in Sec. 3. 2, in this paper we assume the same flat ΛCDM cosmology assumed by the reconstruction in  [38] , with Ω r = 0, Ω K = 0, Ω m = 0. 2889, Ω b = 0. 048597, Ω Λ = 0. 7111, w = -1, n s = 0. 9667, σ 8 = 0. 8159, H 0 = 67. 74 kms - 1  Mpc -1. The paper is structured as follows. In section Sec. 2, we describe the datasets used in this work. After formulating the physical models and statistical methods to be applied on the data in section Sec.
For this estimate of the kSZ signal, the primary noise source for large filter sizes is still the primary CMB, while for small filter sizes -where only a very limited number of pixels are encompassed by the inner disk or outer ring -the instrumental noise dominates. Our method of extracting the kSZ flux in this paper is similar to that in  [6, 12, 28, 29, 40]. It is worth mentioning here that the typical apparent size of maxBCG clusters selected for our analysis is very close to the Planck beam (see Sec. 2. 1 and Sec. 2. 2. 1). We defer a more optimal filtering method (which would require more specific assumptions on the form of the gas profile), such as the matched filter [see, e. g. 13, 26, 41] , to applications on CMB data with higher resolutions.
85 × 10 14 M from our analysis (see App. A). Next, we select only clusters within regions where the borg-SDSS3 reconstruction are well-constrained by data, i. e. sky regions where LOWZ and CMASS galaxies are actually observed. In addition, we remove clusters outside of the Planck 2018 common confidence mask recommended for temperature analysis  [36]. θ eff,i = θ 2 200,i + θ 2 beam (2. This leaves us with a final sub-sample consisting of 3512 clusters from the original maxBCG catalog. We show in Fig. 1  a HEALPix map 5   [44, 45]  of these clusters in Galactic coordinates. We further divide our cluster sample into two datasets: spec-z: this set includes 908 clusters whose brightest member galaxies (BCG) have spectroscopically determined redshifts, denoted by z spec ; photo-z: this set includes 2604 clusters for which only photometric redshifts as inferred by the maxBCG algorithm, denoted by z photo , are available.
The mean and median mass of our cluster sample (including both datasets) are M mean 200 = 7. 18×10 13 M and M median 200 = 7. 31×10 13 M , while the mean and median redshift are z mean = z median = 0. 257. This results in a mean and a median apparent size of θ mean eff = θ median eff = 3. 9. The full histograms of redshift and apparent angular size for both sets of clusters are shown in the left and right panels of Fig. 2 , respectively. The apparent size distributions shown in right panel of Fig. 2  are rather concentrated; their standard deviations are ∼ 0. 25 arcmin. This actually implies that the AAP filter would not yield significant improvement, as compared to the traditional AP filter, for both sets of cluster considered here. We nevertheless adopt the former for the individual-scale measurements, as already noted in Sec.
Individual-scale signal measurements We show in Fig. 8  the inferred value of α ϕ f n as a function of the AAP filter scale ϕ f , using spec-z set alone (top panel) or photo-z set and both sets (bottom panel). We emphasize again that the 1σ uncertainty shown here as shaded band and reported in Tab. 1 includes also uncertainties in the reconstructed large-scale velocity field. As can be noted from Fig. 8 , we obtain consistent results between the two datasets specz and photo-z, which can be combined as shown in red in the bottom panel of Fig. 8. Note that the slightly larger uncertainty region of α n measurement using spec-z set -compared to that of α n measurement using photo-z set -is mostly caused by their limited quantity, as suggested by the ratio between the two uncertainties being approximately constant across all filter scales.
Both panels of Fig. 8  show that most of the information come from small scales. As the filter scale increases, the AAP estimate picks up more and more contribution from primary CMB anisotropies, as well as other large-scale sources of contamination, and quickly loose its constraining power. In addition, we provide the significance at each filter scale for each dataset in Tab. 1. All sets show peaks of significance at ϕ = 0. 9, close to the angular cluster scale as one should expect. Further, for the photo-z set, the photo-z uncertainty shows a bigger impact at small filter sizes, which is also an expected behavior, since on large scales, the primary CMB is still the dominant noise source. For illustration, we additionally show the mixture weights (cf. Eq. (3. 8)) in Fig. 9 , since it can provide some insights on how the information in spec-z and photo-z sets are combined.
It is then reasonable to suspect that the photo-z fluctuations are not symmetrically distributed around the inferred value by the maxBCG algorithm, but rather biased towards overdense regions along the respective line of sight. Further detailed investigation is thus required to identify the optimal way to combine information from both spectroscopic and photometric data, subject to current constraints on computational resources. We defer such an investigation to future work. The significance of the cumulative combined signal for each dataset is summarized in Tab. 2. As in Tab. 1, most of the information is limited to filter sizes below and around the apparent size of maxBCG clusters in our samples. Above that scale, CMB noise severely limits our significance. This suggests that data from current CMB experiments such as ACTpol and SPT-3G with their much higher resolutions, ∼ 1 arcmin  [58, 59] , can improve our significance significantly.
On the other hand, even at the modest resolution of ∼ 2 -3 arcmin, we expect a future experiment such as CMB-S4, with much lower instrumental noise, to also yield a significantly improved measurement. In both cases, the details of the gas profile would become important and one would almost certainly need to go beyond the Gaussian profile and the AP filter case considered here. The posterior mean of the kSZ combined signal amplitude α n ± 1σ α measured at increasing maximum AP filter size, and the corresponding detection significance (cf. Eq. (3. 14)) are shown for spec-z, photo-z and both datasets (left to right). The maximum significance in each case is highlighted. Note that, for readability we only list here results for θ f =  [3, 7]  arcmin. ϕ f spec-z photo-z spec-z + photo-z α ϕ f n sign.
Null tests for systematics In this section, we further assert the significance of our measurement by performing two null tests on mock data in which we 1. shuffle the position of the clusters in our analysis, and 2. replace the SMICA2018 by a set of 300 SMICA-like mock maps, also taken from Planck 2018 data release  [36]. Since the photo-z set adds very little information, in the tests below, we will only focus on the spec-z set. We have found that the significance computed from Eq. (3. 14) can be rather well approximated by the simple ratio S/N= α ϕ f n /σ α. Below, we employ this approximation to ease the numerical computation of the significance in these null tests.
Null tests: Individual-scale signal For the case of individual-scale measurements using the AAP filter, we measure a signal amplitude consistent with zero for the spec-z set with cluster positions being shuffled, as can be seen in the left panel of Fig. 11. When applying our pipeline on the set of 300 SMICA2018 simulations (including CMB and instrumental noise) provided by Planck  [36] , we also recover S/N ratios consistent with zero-mean Gaussian distributions. We show in the top panels of Fig. 12  the histograms of S/N for increasing individual filter scale from ϕ = 0. 9 to ϕ = 1. 1. The histogram of ϕ = 0. 9 (top row, left panel) appears consistent with our reported significances of 1. 7 for the spec-z dataset (cf. Tab. 1).
In this simple demonstration, the sensitivity of our kSZ measurement is limited by several factors, including the simple AP filter approach, the typical cluster scale being close to the resolution of the SMICA2018 map, and the current level of instrumental noise. Yet, we find that ignoring systematic uncertainty in the reconstructed velocity could already lead to a significant bias in the kSZ measurement (cf. Eq. (3. 12) and Fig. 4 ). It would be interesting to study how this would affect kSZ measurements using more sophisticated filtering methods, for example, the matched-filter approach, and data from higher resolution and sensitivity CMB experiments, e. g. CMB-S4  [20] , SPT-3G  [62]. For the sole purpose of kSZ detection, another important factor is the sheer number of clusters. In fact, our main dataset, the maxBCG spec-z sample, is quite limited in number at only 908 clusters.
Acknowledgments We would like to thank  Eiichiro Komatsu ,  Jochen Weller ,  Franz Elsner ,  Alex Barreira ,  Giovani Cabass ,  Seunghwan Lim ,  Emmanuel Schaan ,  Naonori Sugiyama ,  Daisuke Nagai ,  Shun Saito , as well as the members of the  Aquila Consortium  for inspiring and helpful discussions. MN and FS acknowledge support from the  Starting Grant  ( ERC-2015-STG 678652 ) "GrInflaGal" of the  European Research Council. GL acknowledges financial support from the  ILP LABEX  (under reference  ANR-10-LABX-63 ) which is financed by  French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02. This work was supported by the  ANR   BIG4  project, grant  ANR-16-CE23-0002  of the  French Agence Nationale de la Recherche. This work is done within the  Aquila Consortium.
Traditional static embeddings, such as word2vec and Fasttext, assume that each word's meaning can be represented as a single vector, independently of its context. While generic and reusable, these models usually conflate the different meanings of a given unit into a single vector  (Camacho-Collados and Pilehvar, 2018). Contextual models, such as ELMo, GPT-2, BERT, and their variants, encode each word's occurrence as a context-dependent vector, assuming that each context corresponds to a different sense  (Yarowsky, 1993). In short, while static models create one generic embedding per lexical unit, contextual models provide a fine-grained distinct representation for each occurrence. Both models, but especially the latter, are increasingly complex and opaque  (Rogers et al. , 2020) , requiring advanced techniques to help humans understand their strengths and limitations  (Jawahar et al.
Experimental Setup We describe in this section the data we have used to build contextual and lexical signatures and the data we will use in the following section to evaluate our method. Supersense Tagset To guarantee a sufficient scope (with respect to the size of the seed lists) and to simplify analysis, we grouped Wordnet supersenses into six coarser categories for our experiments on French nouns (details in Appendix A). Animate entity (ANI) includes nouns referring to living and animate entities, namely persons (e. g. , agriculteur 'farmer') and animals (e. g. , chiot 'puppy'); Natural object (NAT) is for nouns referring to natural entities (e. g. , étoile 'star'), plants (e. g. , peuplier 'poplar tree') and body parts (e. g. , hanche 'hip'); Manufactured object (MAN) is composed of nouns denoting human-made or transformed entities: artifacts (e.