dataesr/scientific-paragraphs-categorization huggingface dataset:
2021b ). More broadly, whilst certain invasive species might be associated with concomitant economic benefits to certain sectors, the lack of synthesis of invasion benefits remains a knowledge gap that precludes formal comparison with costs. However, we suspect that any benefits would be magnitudes lower than reported costs. The observed data limitations are not restricted only to aquatic species. Another currently infamous example that is gaining increasing attention is the introduced insect Ctenolepisma longicaudata (long-tailed silverfish), which is causing substantial economic and cultural losses of museum material  (Thomsen et al. 2019). Other examples of invasive species causing consistent damages in Germany are rodents, such as the racoon Procyon lotor, which are, however, not recorded in InvaCost.
Acknowledgements The authors acknowledge the  French National Research Agency  ( ANR-14-CE02-0021 ) and the  BNP-Paribas Foundation Climate Initiative  for funding the  InvaCost  project that allowed the construction of the InvaCost database. The present work was conducted following a workshop funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  AlienScenario  project funded by  Bio-divERsA and Belmont-Forum  Call 2018 on biodiversity scenarios. RC is funded by the  Alexander von Humboldt Foundation. MG and CD are funded by the  Biodi-vERsA-Belmont Forum  Project " Alien Scenarios " ( BMBF/PT DLR 01LC1807C ). The authors acknowledge  Antonin Kouba  for the translation of the abstract to Czech and  Elena Angulo  for the translation to Spanish. Lastly, the authors would like to acknowledge the meaningful effort from the anonymous reviewers.
Introduction In spite of AlphaGo  [10] , some games still resist to computers and for many games the computational requirement is still huge. We present Polygames, our open-source zero learning framework available at https://github. com/ facebookresearch/polygames. It is based on Zero learning, combining Monte Carlo Tree Search and Deep Learning. It features a new architecture for accelerating the training and for making it size-invariant (Section 2. 1). It allows neuroplasticity i. e. adding neutral layers, adding channels, increasing kernel size (Section 2. 2) and warm start. Polygames also features a new tournament mode for robustifying the training (Section 2. 3). The framework provides a single-file API, that is generic enough for implementing many games, and comes with a library of games and many checkpoints.
Other open-source frameworks Many teams have repeated and sometimes improved the Alpha Zero approach for different games. Elf/OpenGo  [12]  is an open-source implementation of AlphaGo Zero for the game of Go. After two weeks of training on 2,000 GPUs it reached superhuman level and beat professional Go players. Leela Zero  [8]  is an open-source program that uses a community of contributors who donate GPU time to replicate the Alpha Zero approach. It has been applied with success to Go and Chess. Crazy Zero by Rémi Coulom is a zero learning framework that has been applied to the game of Go as well as Chess, Shogi, Gomoku, Renju, Othello and Ataxx. With limited hardware it was able to reach superhuman level at Go using large batches in self-play and improvements of the targets to learn such as learning territory in Go.
Neuroplasticity Several modifications are almost neutral when initialized close to zero: • addition of residual blocks (i. e. switching from 12 blocks of 3 convolutional layers to 13 or 14 blocks of 3 convolutional layers); • addition of new channels; • extension of the kernel size (from 3 × 3 to 5 × 5 or 5 × 5 to 7 × 7, etc). Polygames provides a script "convert" that makes such a growth of the neural network easy. Training can be resumed after such extensions of the neural architectures; we can train, then grow (while preserving the quality of the model as it remains almost equal to the previous model as new weights are close to 0), then resume the training with more degrees of freedom.
Conclusions We propose a state-of-the-art framework, called Polygames, that can play to various games. It is based on zero learning, with innovations detailed in Section 2, and had success stories detailed in Section 3. Polygames contains new architectures, allows architecture search thanks to neutral components addition and is stabilized by a tournament mode and an overfitting fighting method. It was widely tested in the TAAI competition and on LittleGolem (www. littlegolem. net). The source code is publicly available under an opensource license.
Background: Proteogenomics aims to identify variant or unknown proteins in bottom-up proteomics, by searching transcriptome-or genome-derived custom protein databases. However, empirical observations reveal that these large proteogenomic databases produce lower-sensitivity peptide identifications. Various strategies have been proposed to avoid this, including the generation of reduced transcriptome-informed protein databases (i. e. , built from reference protein databases only retaining proteins whose transcripts are detected in the sample-matched transcriptome), which were found to increase peptide identification sensitivity. Here, we present a detailed evaluation of this approach. Results: First, we established that the increased sensitivity in peptide identification is in fact a statistical artifact, directly resulting from the limited capability of target-decoy competition to accurately model incorrect target matches when using excessively small databases.
generated by appending variant sequences from public genomic repositories (e. g. , COSMIC or dbSNP) to the reference database  [13] [14] [15]  or adding sample-specific variants identified from matched transcriptomes or genomes. In the second case, protein databases can be generated by 6-frame translation of the genome or sample-matched transcriptome  16, 17. A major downfall of these proteogenomic databases is that they tend to be very large. Searching very large databases comes at a considerable computational cost, and complicates the task of discriminating between correct and incorrect matches. In particular, various studies have shown that when using target-decoy competition (TDC) for FDR control on large database searches, fewer peptides are identified at the same FDR level. This contrasts starkly with the initial reason for the development of proteogenomics  [18] [19] [20].
To avoid reducing the number of identifications, it was proposed that FDR validation of canonical and novel peptides should be performed separately, and that post-search filters or machine-learning methods should be applied to increase confidence in the newly-identified peptides  [13] [14] [15] 21, 22. In ad ition, various strategies were adopted to limit the size of databases generated by proteogenomics. When possible, 6-frame genome translation was replaced by translation of candidate ORFs identified by gene prediction algorithms. Alternatively, sample-specific variants from matched sequencing were preferentially added to the reference database rather than variant sequences from COSMIC or dbSNP  [23] [24] [25]. In some studies, after appending variants identified from sequencing data, the reference database was reduced to contain only proteins for which transcripts were detected by transcriptomics, since according to the "central dogma of biology", there can be no protein without the corresponding transcript  [26] [27] [28].
Yet other groups proposed to generate reduced transcriptome-informed protein databases by barely reducing the reference database to proteins for which transcripts were detected, without including any novel sequences  26, 28, 29. The only declared objective of this approach is to increase the sensitivity of identification of known sequences. Indeed, the authors claim that searching such reduced transcriptome-informed databases can increase the number of valid identifications. A strategy was also proposed to optimize the balance between identifications lost due to the incompleteness of an excessively reduced database and additional identifications made from searches of reduced transcriptome-informed databases, so as to maximize the number of valid identifications  30. However, in all these studies: i.
Only limited attention was given to the mechanistic explanation for the increased number of peptides identified searching smaller databases; ii. Little is known about how reduced transcriptome-informed database searches affect protein inference in terms of ambiguity of protein identification and shared peptide assignments. Therefore, in this article, we investigated the use of reduced transcriptomeinformed sample-specific protein databases, focusing on these two methodological aspects. Our investigations led to three conclusions. First, the reported increase in the number of identifications obtained by searching a reduced transcriptome-informed database is a statistical artifact: it is simply the spurious consequence of an underestimated FDR, which results from the sensitivity of TDC to database size (also reported in  31  ).
In other words, reducing the size of the database searched to increase sensitivity broadly amounts to validating peptide identifications at a higher-than-reported FDR. This observation consequently raises questions as to the validity of peptides that are only identified when searching the reduced database, and as to cross-study comparability. However, searching reduced transcriptome-informed protein databases followed by accurate FDR control remains nonetheless an interesting strategy, as it decreases the ambiguity of protein identifications by reducing the proportion of shared peptides and the size of protein groups. Finally, searches against the reference database followed by posthoc filtering of proteins for which there is no evidence of transcript expression provides comparable proteomic identifications to searches against the reduced transcriptome-based database.
Reduced transcriptome-informed database search does not increase sensitivity if FDR is accurately controlled To investigate how searching reduced transcriptome-informed protein databases affects proteomic identifications, we used two human samples (hereafter referred to as Jurkat and Lung) for which matched transcriptome and proteome data were publicly available (Additional File 1: Table  S1 ). For each of them, we built a sample-specific reduced transcriptome-informed protein database, as follows. First, we processed transcriptome datasets and identified the set of transcripts expressed in each sample using StringTie, a common transcriptome assembly method (see "Transcriptome analysis" in Methods). Then, we generated reduced databases for use in MS/MS searches by retaining from the Ensembl human protein database only proteins for which transcripts were expressed in the sample-matched transcriptome (see "Construction of reduced transcriptome-informed protein databases for MS/MS searches" in Methods) (Figure  1A-B ).
We compared valid peptide-spectrum matches (PSMs) obtained from the MS/MS search against the whole Ensembl human database (referred to as the "full database") or against the sample-specific reduced database (referred to as the "reduced database") at 1% FDR, as estimated by TDC. In agreement with previous studies  26, 28, 30  , we found that a few spectra and peptides identified in the full database were lost when searches were performed against the reduced database ("lost in reduced DB"), whereas others were only identified in the reduced database search ("additional in reduced DB") (Figure  1C ). Lost identifications are due to incompleteness of the reduced database. Indeed, even more identifications were lost when using a further reduced protein database, such as that generated on the basis of the smaller set of expressed transcripts identified by Cufflinks, an alternative method of transcriptome assembly (Additional File 2: Fig.
S1 ). Additional identifications, in contrast, are commonly ascribed to increased sensitivity of MS/MS searches when using smaller databases, such as reduced transcriptome-informed protein databases  26, 28, 30. In this study, we investigated the origin of these additional identifications more thoroughly. To do so, we performed a detailed comparison of all (target or decoy) PSMs retained from the full and reduced database searches, after applying validation prefilters (i. e. , single best-scoring PSM for each spectrum, minimum peptide length of 7 amino acids), but before filtering at 1% FDR (Figure  1A ). Since the reduced database was constructed as a simple subset of the Ensembl human database and considered only a single best-scoring peptide for each spectrum (see "Proteome analysis" in Methods), we could easily map each spectrum match between the two searches.
Two interesting observations emerged from this comparison. First, several spectra were reallocated in the reduced database (i. e. , assigned to different matches in the reduced database and the full database). This phenomenon occurred when the peptide match from the full database was not present in the reduced database. However, the PSM score was never higher in the reduced database. At best, it was equal (data points on the diagonal, Figure  2A , Additional File 2: Fig. S2A ) to the score obtained in the full database, but for the most part it was lower (Figure  2B , Additional File 2: Fig. S2B ). Therefore, additional identifications in the reduced database do not come from an improved search score. For the sake of clarity, we emphasize that the few spectra which matched only sequences in the reduced database (indicated as "no match, target" or "no match, decoy" in Figure  2A , Additional File 2: Fig.
S2A, S3A, S4,  and S5 ) do not contradict this observation; they can all be explained by reallocation and the prefilters applied for validation (Additional File 2: Fig. S3B , see Additional File 3: Supplementary Note 1 for a detailed explanation). The second main observation was that the score cutoff estimated by TDC at 1% FDR (i. e. , the score defining the set of accepted PSMs, while respecting the constraint that less than 1% of them are expected to be false discoveries) was lower for the reduced database than for the full database search (Figure  2A , Additional File 2: Fig. S2A ). Consequently, for a few spectra no match was validated after FDR control in the full database, whereas, at a lower or at most equal score, the match was validated in the reduced database search (pointed out by the arrow in Figure  2A  and Additional File 2: Fig.
S2A ). This clearly explains why these PSMs are accounted for as additional identifications in the reduced database search. We also observed a few reallocations, which can likewise yield additional spectra and/or peptide identifications in the reduced database search. These cases are, in particular, reallocations from non-target matches in the full database to target matches in the reduced database search (2. 9% and 1. 9% of all spectra for Jurkat and Lung, respectively) and reallocations between different target matches (3. 2% and 2. 7% of all spectra for Jurkat and Lung, respectively) (Figure  2A ,C and Additional File 2: Fig. S2A, C ). However, only a minority of these reallocations are valid identifications when applying 1% FDR control (i. e. , pass the score cutoff for FDR control from the reduced database search) (Figure  2C , Additional File 1: Table  S2 , Additional File 2: Fig.
S2C ). Furthermore, even fewer of them would pass the cutoff determined for the full database search, and they are hereafter referred to as "pure reallocations" to indicate that additional identifications from these PSMs originate solely from reallocation and they do not represent additionally validated PSMs due to the lower cutoff for 1% FDR validation in the reduced database (Figure  2C , Additional File 1: Table  S2 , Additional File 2: Fig. S2C  and S6A). Additional peptide identifications originating from either a lower cutoff for FDR control or from pure reallocations had lower PSM scores compared to peptides identified in both database searches (Additional File 2: Fig. S6B ). For pure reallocations, the difference in score between the full and reduced database match was sometimes quite large, especially for target PSMs in the full database that were reallocated in the reduced database search (Additional File 2: Fig.
S6C ). Furthermore, additional peptide identifications only allowed 6 and 8 supplementary protein identifications (i. e. , protein groups for which protein members were not identified in the full database search) in the Jurkat and Lung samples, respectively (Additional File 1: Table  S3 ). Thus, these additional identifications are of lower quality and provide little benefit in terms of protein identification. Overall, only a small number of additional identifications were obtained thanks to pure reallocations; they were mainly obtained due to the lower cutoff for FDR control in the reduced database. Thus, the lower cutoff explained 98. 6% (n=3,147) and 95. 2% (n=1,875) of additional spectral identifications and 96. 5% (n=5,560) and 77. 5% (n=1,524) of additional peptide identifications for the Jurkat and Lung samples, respectively (Figure  2D-E , Additional File 2: Fig.
S2D-E ). We next investigated the reasons why lower cutoffs were observed at a same FDR threshold in the reduced databases. To do so, we first simulated the outcome if the cutoff were instead equal to that of the full database (Figure  3A , Additional File 2: Fig. S7A ). In these conditions, the proportion of valid decoys in the reduced database search was observed to considerably decrease compared to the full database, with a net loss of 38. 4% of valid decoys in the Jurkat sample (Figure  3B ) and 27. 1% in the Lung sample (Additional File 2: Fig. S7B ). Indeed, a significant fraction of spectra matching valid decoys in the full database were assigned to invalid or non-decoy matches in the reduced database, and were not counterbalanced by reallocations in the other direction (i. e. , from invalid/non-decoy matches to valid decoys) (Figure  3C , Additional File 2: Fig.
S7C, S4,  and S5 ). In contrast, the majority of spectra matching valid targets in the full database matched the same valid target in the reduced database, so that their loss was quite limited (Figure  3C , Additional File 2: Fig. S7C ). Although spectra matching valid decoys in the full database were reallocated much more frequently in the reduced database than spectra matching valid targets, upon reallocation they behaved similarly: few reallocations resulted in a valid match of the same type (Figure  3C , Additional File 2: Fig. S7C ), and the difference in score between full and reduced database matches was comparable (Additional File 2: Fig. S8A ). Hence, the proportion of valid decoys lost in the reduced database was higher than that of targets lost, simply because a higher proportion of decoys was reallocated.
This observation can easily be explained by how the reduced database was generated: only proteins whose transcript is expressed, thus those more likely to be present, were retained from the canonical full protein database. Therefore, all valid targets from the full database are theoretically still present in the reduced database, but the same cannot be said for decoys, that by definition represent random matches (Additional File 2: Fig. S8B ). The lower cutoff obtained by TDC for the reduced database allows a few more decoys to be validated and thus recovers the proportion of valid decoys required to declare a nominal FDR level of 1% (Figure  3B , Additional File 2: Fig. S7B ). We suggest that the additional identifications validated at a lower cutoff in the reduced database are simply a byproduct of the known influence of database size on TDC  31  , rather than evidence of increased sensitivity when searching reduced databases.
Naturally, in the absence of a benchmark, it is impossible to determine whether these identifications represent correct matches that were missed in the full database due to FDR overestimation, or incorrect matches that were accepted in the reduced database due to FDR underestimation. Nevertheless, three main observations indicate that they should at least be considered with caution. First, these matches were accepted in the reduced database at quite low scores, meaning that, in any case, they represent low-quality spectra that cannot be identified with any great confidence. Second, it could be assumed that additional identifications arise due to the removal, in the reduced database, of high-scoring decoys that out-compete correct target matches, thus reducing sensitivity. However, in our study, most of the additional identifications did not represent reallocations from decoys to targets; rather, they consisted in the same PSMs that were accepted at 1% FDR only in the reduced database due to the lower score cutoff applied (Figure  2D-E ; Additional File 2: Fig.
S2D-E ). Third, and most importantly, the artifactual origin of the additional identifications, given TDC sensitivity to database size, is supported by the comparison between the behavior of TDC and of the Benjamini-Hochberg (BH) procedure for FDR control  32. The BH procedure is known to yield a conservative and stable FDR control, and it was recently successfully applied to peptide identification  31. In particular, TDC was found to be less conservative and less stable than BH with respect to preliminary filters on precursor mass accuracy: at a narrower mass tolerance, fewer decoys were fair competitors for incorrect random matches, and consequently cutoffs were artificially lowered. Therefore, reducing the database size can similarly result in an insufficient number of decoys to accurately simulate incorrect target matches, thus leading to the observed lower cutoffs.
To confirm this possibility, we applied the BH procedure to target-only database searches (see "Proteome analysis" in Methods). We obtained more conservative score cutoffs and, most importantly, more stable with respect to database size, compared to TDC and we confirmed this at various levels of FDR control (0. 5%, 1%, 5%) (Figure  3D , Additional File 2: Fig. S7D, S9A-B ). In line with this result, a much more limited number of additional identifications was validated in the reduced database searches when using the BH-based FDR control (Additional File 2: Fig. S9C ). We also employed the BH procedure for FDR control on concatenated target-decoy database searches; while doing so is nonsensical from a practical data-processing viewpoint, from a statistical methodology viewpoint, it simplifies comparisons between BH and TDC stabilities.
Transcriptome information helps to reduce ambiguity of protein identifications Although they do not enhance the sensitivity of peptide identifications, reduced transcriptome-informed databases can still be of benefit in proteomics at the proteininference step, as they reduce the ambiguity of protein identifications. These databases include fewer proteins -only the proteins which are most likely to be present given their transcript expression -and it is reasonable to assume that with fewer possible protein matches, we might obtain fewer shared peptides and smaller protein groups. Such a decrease in protein group size has already been observed, but was either not discussed  33  , or was attributed to an additional number of identifiable peptides available for parsimonybased protein inference  26.