dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion and conclusions We have compared the largest available, consistent database of daily gridded land-based precipitation products for their representation of a range of annual precipitation indices and extremes. We conclude that taken on global average products can appear reasonably similar in terms of their spatial patterns but in terms of the range of values of precipitation extremes over space and time they can have quite different forms. Splitting the 22 products available from the FROGS database that met our selection criteria into products clusters we found that in situ-based products are most similar to each other compared to other product clusters and reanalyses are least similar. The largest differences between products occur in the tropics, the driest regions and areas with high topographic contrasts (where there is limited in situ data) and differences are particularly marked in South-East Asia and Africa.
In addition, we have not discussed in detail the problems associated with remote sensing of precipitation extremes (such as instrument sensitivity to capturing all types of precipitation or retrieval method uncertainties) or the fact that satellites measure instantaneous rain rates while the in situ products generally measure 24 h accumulations. Another issue is the mismatch between stationbased products which convert points to grids and areal average precipitation (such as produced by reanalyses). Indeed we broadly assume that the indices calculated from each product are meaningful and directly comparable between the product types. Also we have not included dataset uncertainties in this analysis (that is the uncertainties that are often provided by the dataset developers associated with their products) mainly due to the fact that not all datasets come with such information and/or what information is provided e.
g. interpolation errors might not be comparable across products. It should also be remembered that FROGS is a 'living database' so, for example, not all freely available products are currently included. One perspective of this work would be to refine the clustering of the products (using new products added to FROGS as they become available, like IMERG from NASA, CMORPH-2 from NOAA or precipitation from ERA-5) in particular to explore within the satellite ensembles the sensitivity to the use of constellation data versus single platform. This could further reduce the cluster spread as shown for the tropics over a short period of time  (Roca 2019 this Focus Collection). However, based on the information that we have gathered we can make some recommendations on the use of global products for understanding observed precipitation extremes.
This should give us some comfort in at least the robustness of the sign of the thermodynamic component of global precipitation extremes trend estimates if not their magnitude. • Lastly, and not surprisingly, we have more confidence in regions with an abundance of in situ observations and conversely our largest uncertainties are over regions that are poorly sampled. In the latter regions, it is possible that the uncorrected satellite products might offer a generally more realistic view of precipitation extremes in these regions due to problems with bias correction algorithms, although this is yet to be thoroughly tested. Overall our results indicate that satellite-derived precipitation datasets, if properly assessed, could provide useful information to inform long-term trends and can fill in useful gaps in regions with limited gauge density if long-term homogeneous satellite-based data can be provided.
Acknowledgments LVA, MB and PLN are supported by the  Australian Research Council (ARC)  grants  DP160103439  and  CE170100023. MGD was supported by the  Spanish Ministry for the Economy, Industry  and  Competitiveness Ramón  y Cajal 2017 grant reference RYC-2017-22964. Indices were calculated using the climpactv2 software (https://climpact-sci. org/get-started)and all analyses and graphics were produced using the  NCAR Command Language  ( NCL 2013 ).
Herold et al 2016, Beck et al 2017, Herold et al 2017 and regionally e. g. Contractor et al 2015, Harrison et al 2019, Kim et al 2019) or have analysed multiple products but with limited focus on extremes (e. g. Sun et al 2018). As part of the International Precipitation Working Group (IPWG; http://isac. cnr. it/~ipwg/), GEWEX Data and Analysis Panel (GDAP; https://gewex. org/panels/ gewex-data-and-analysis-panel/), and World Climate Research Programme Grand Challenge on Weather and Climate Extremes (WCRP GC Extremes; https://wcrpclimate. org/gc-extreme-events), a database has been developed which enables a comparison of multiple products on a daily 1°×1°resolution.
Sea ice model component RIOPSv2 uses the Los Alamos Community Ice CodE version 4. 0 (CICE;  Hunke, 2001; Lipscomb et al. , 2007; Hunke and Lipscomb, 2008). CICE is a dynamic-thermodynamic sea ice model on an Arakawa B-grid. Ice-ice interactions are governed using an Elasto-Viscous Plastic scheme  (Hunke et al. , 2001). In RIOPS, CICE is configured to use 10 ice thickness categories and a single snow layer. Landfast ice is parameterized using the basal-stress approach of  Lemieux et al. (2015 Lemieux et al. ( , 2016b)). In RIOPSv2 the model code and settings were kept identical with the exception of the atmosphere-ice and ice-ocean drag coefficients (Table  1 ). In an attempt to reduce the existing negative bias in ice velocity, the ice-ocean roughness was reduced from 2. 6 cm to 2. 0 cm, and the air-ice roughness increased from 0.
T/S profile innovations Innovation statistics obtained from in situ temperature and salinity profiles averaged over the upper 500 m of the water column and over the range 500-2000 m are shown in Figs. 11-14. These observations are composed mainly of data from Argo profiling floats, with some additional profiles from field campaigns, moorings, voluntary observing ships, gliders and marine mammals. Interestingly, the period of evaluation includes the Year of Polar Prediction  (2017 -2019; Jung et al. , 2016)  for which a number of additional in situ ocean observations were deployed  (Smith et al. , 2019b). These include Argo and ALAMO floats  (Wood et al. , 2018)  used seasonally during ice-free periods as well as Ice Tethered Profilers (ITP;  Toole et al. , 2011). These, together with other additional ocean observations deployed during YOPP, provide an exceptional opportunity to evaluate water mass properties in RIOPS and GIOPS over Arctic Ocean, for which a significant gap is usually present in the in situ ocean observing network.
As shown for SLA and SST, the largest innovations in profile data for both systems are found in the Gulf Stream region due to the strong mesoscale variability and important spatial variability in water mass properties (Figs. 11  and 13 ). Significant innovations in salinity are also found along many coastal regions. On the Canadian east coast, in the Gulf of St. Lawrence and along the Labrador Coast, RMS salinity innovations often exceeding 0. 5 psu are present. Similar errors are present around the North Sea and along the Norwegian coastline with slightly larger values in RIOPSv2. The larger biases in RIOPSv2 may be associated with a positive salinity bias in the upper 50 m of the water column (Fig. 12 ). This bias may be due to a deficit in river runoff together with the associated reduction in vertical stratification resulting in overly intense vertical mixing due to tides.
e. to include fortnightly and monthly) without erroneously filtering mesoscale variability. As part of YOPP (2017-2019), a significant number of in situ temperature and salinity observations were taken and made available via the Global Telecommunications System allowing their use in studies such as these that rely on operational databases. These valuable observations provide a rare glimpse at errors in Arctic water mass properties  (Smith et al. , 2019b). Here, we see RIOPSv2 shows larger salinity errors in the Beaufort Sea, with salinity biases of 0. 3-0. 4 psu in the eastern As the largest innovations in both RIOPSv2 and GIOPSv3 occur in the Gulf Stream region, a comparison of SLA along a particular Jason satellite track was made. Both systems capture the main evolution of mesoscale variations, however, RIOPSv2 shows smaller differences from SLA observations than GIOPSv3 (Table  3 ) with higher correlation (0.
82 and 0. 73 respectively) and lower RMS (12 cm and 14 cm respectively). To investigate the role of increased resolution in RIOPSv2, the PSD of surface kinetic energy over the Gulf Stream region was investigated. The PSD of RIOPSv2 is 1. 7 times the power of GIOPSv3 and contains smaller-scales down to roughly 35 km as compared to 66 km for GIOPSv3. These scales give an indication of the effective model resolution and correspond roughly to 5 and 3 grid points respectively. These values are somewhat smaller than one would expect purely from a numerical point of view (generally the smallest feature resolvable requires 6-10 grid points) and suggests the data assimilation may be artificially increasing the resolution somewhat. This is not surprising for GIOPSv3, as it has only a 1/4° grid resolution and is thus considered eddy-permitting only.
Acknowledgements The authors acknowledge the support of  Pierre Pellerin ,  Hal Ritchie ,  Youyu Lu  and other members of the  CONCEPTS consortium. They also thank  Jean-Philippe Paquin  for the suggestion to examine SLA Hovmöller diagrams. They also acknowledge the support of the informatics and operational implementation groups at CCMEP. In situ temperature and salinity profile data from Argo floats used here were collected and made freely available by the  International Argo Program  and the national programs that contribute to it. (http://www. argo. ucsd. edu,http://argo. jcommops. org). The Argo Program is part of the Global Ocean Observing System. This study has been conducted using E. U. Copernicus Marine Service Information. This study is a contribution to the  Year of Polar Prediction (YOPP) , a flagship activity of the Polar Prediction Project (PPP), initiated by the  World Weather Research Programme (WWRP)  of the  World Meteorological Organisation (WMO).
Background. Recent advances in sequencing technologies have led to an explosion in the number of genomes available, but accurate genome annotation remains a major challenge. The prediction of protein-coding genes in eukaryotic genomes is especially problematic, due to their complex exon-intron structures. Even the best eukaryotic gene prediction algorithms can make serious errors that will signi cantly affect subsequent analyses. Results. We rst investigated the prevalence of gene prediction errors in a large set of 176,478 proteins from ten primate proteomes available in public databases. Using the well-studied human proteins as a reference, a total of 82,305 potential errors were detected, including 44,001 deletions, 27,289 insertions and 11,015 mismatched segments where part of the correct protein sequence is replaced with an alternative erroneous sequence.
As a result, nucleotide and protein repositories, such as UniProt  [9] , RefSeq  [10]  or Ensembl  [11] , contain an increasing number of sequence errors or inaccuracies  [12, 13]. Commonly encountered errors include missed proteins  [14, 15] , wrong exon and gene boundaries  [16] , non-coding nucleotide sequence retention in coding exons  [17] , as well as fragmentation or fusion of gene models  [18]   (Guigó et al. , 2006). These errors can be propagated in downstream applications, leading to incoherent or incorrect conclusions  [19, 20]. Errors can severely affect studies of individual proteins, including transcript quanti cation, phylogenetic tree inference, and protein structure or function predictions. It has also been shown that gene prediction errors can lead to biases in large-scale statistical studies  [19, 21, 22].
Therefore, DNA and protein sequence quality is essential and sequence information needs to be accurate, reliable, and accessible in a clear and consistent manner  [23, 24]. Various strategies have been developed to help identify and correct errors in gene annotations, using information from comparative or evolutionary analyses  [25] [26] [27] , experimental evidence from expressed sequence tags (ESTs) or RNA-sequencing (RNA-seq)  [22, 28] , or dedicated sequence datasets such as the G3PO benchmark  [29]. These studies generally agree that up to 50% of the protein sequences in the public databases have an least one error. However, very few studies have attempted to identify the underlying reasons for the badly predicted sequences. Here, we use a large set of 176,478 proteins from ten primates in order to characterize different types of gene prediction errors and investigate their causes.
The protein sequences were downloaded from the Uniprot reference proteomes and RefSeq databases. To identify potential sequence errors, including insertions, deletions and mismatched segments (illustrated in Fig. 1 ), we compared the primate sequences to the canonical isoforms of all known human proteins in the UniProt database. The UniProt canonical sequence for a given human protein generally corresponds to the most frequent or most conserved protein isoform in orthologous species. We then focused on the primate protein sequences containing mismatched segment errors, i. e. where part of the correct protein sequence is replaced with an alternative erroneous sequence, as shown in the example in Fig. 1. These errors cause particular problems for downstream applications, since the alignment of badly predicted amino acids can signi cantly affect structural and functional annotations, as well as conservation and phylogenetic analyses.
Orthologous sequences from primate proteomes Potential orthology relationships between human proteins and proteins from ten primates (Pan Troglodytes, Gorilla gorilla gorilla, Macaca mulatta, Macaca fascicularis, Chlorocebus Sabaeus, Papio Anubis, Pongo abelii, Nomascus leucogenys, Callithrix jacchus, Otolemur garnettii) were predicted using a simple protocol based on BLASTP  [34]  searches in the Uniprot reference proteome database. For each human protein sequence, the best hit in each of the ten primates was considered as an orthologous protein if it has an e-value <10 -50 and sequence identity >80%. Table  1  shows details of the primate proteomes used and the number of predicted orthologous sequences. Orthologous sequences were also identi ed using the same protocol in the proteomes corresponding to the ten primates in the RefSeq database (release 98).
Multiple Alignments and Detection of Sequence Errors For each of the 20,596 human proteins, a multiple sequence alignment (MSA) of the primate orthologous sequences was constructed using the MAFFT program  [35]. Each of MSA was analyzed to detect potential insertions, deletions and mismatches in the orthologous sequences using the SIBIS program  [36]. SIBIS uses a Bayesian framework combined with Dirichlet mixture models and visual inspection to identify inconsistent sequence segments representing potential sequence errors. The predicted errors are classi ed into seven categories: N-terminal deletion, N-terminal extension, C-terminal deletion, C-terminal extension, internal deletion, internal insertion and mismatched segment. For this study, we focused on potential sequence mismatches, where a mismatch was de ned as a sequence segment that is at least 20 amino acids long with less than 50% sequence identity between the human reference and the primate sequences.
Evidence that the detected mismatch is in fact a False positive (i. e. the mismatch is due to inaccuracies in the quality control protocol) involves three possible features: the existence of alternative human isoform in the Ensembl database that does not generate a mismatch at that position, MSA alignment errors, mismatches in a sequence region with repeats. The mismatch was characterized as Undetermined if one feature was detected, namely that the same mismatch is identi ed in four or more primates in the MSA, or a mixture of Gene misprediction and False positive evidence was identi ed.
Mismatch error correction protocol For the mismatch errors classi ed as gene mispredictions, a simple protocol was implemented to try to improve the quality of the protein sequence. Given a primate protein sequence with a mismatch error compared to the human sequence, the error correction protocol involves four steps (outlined in Fig. 2 ) as follows: 1. Using the human sequence segment as a query, perform a TBLASTN search in the primate genomic region corresponding to the transcript. 2. If a hit is found with an e-value <0. 001 and sequence identity >80%, it is inserted into the primate transcript as a new exon. 3. Verify the integrity of the new transcript, by checking for overlapping or inverted exons, frameshifts and stop codons in the inserted sequence.
Implementation The analyses were performed using an Sqlite3 database, in-house bash and Python (version 2. 7) scripts, and numpy (numpy. org), pandas (pandas. pydata. org) and matplotlib (matplotlib. org) Python libraries. The Biopython (biopython. org) package was used to perform pairwise alignments during the mismatch characterization step. To retrieve large amounts of data from the Ensembl database, the grequests (pypi. org) package was used to perform asynchronous application programming interface (API) requests. The Json (www. json. org) library was used to process Ensembl API output.
Identi cation of sequence errors in primate proteins To estimate the frequency of gene prediction errors, a large set of primate protein sequences was extracted from the Uniprot reference proteome database. The human proteome was used as a reference, since the proteins have been very widely studied and for the purposes of this study, are assumed to be accurate. By comparing closely related primate sequences with the human proteins, we could identify potential gene prediction errors in the other primate proteomes. Orthologous relationships were predicted using BLASTP searches, where each human protein sequence was used as a query to search each primate proteome. Starting with the 20,595 human proteins, we thus obtained between 14,000 and 19,000 orthologous protein sequences for each primate (Table  1 ).
leucogenys) with as many as 11-12% of their sequences containing at least one mismatch. Surprisingly, the primate showing the highest mismatch rate (N. leucogenys) is not the most distant from human according to the tree of life (Fig. 4B ). Furthermore, G. g. gorilla, which is very close to human in the tree of life, has a medium mismatch rate of 5. 5%. These results taken together show that mismatch detection is not only dependent on the phylogenetic distance between primates and human, and there must be other factors to explain these observations. Finally, to investigate whether the mismatch error rates were an issue related speci cally to protein sequences from the Uniprot database, the same detection protocol was applied using the RefSeq protein database as the source for the primate reference proteomes.
A number of false positive mismatches (37. 9%) were found that could not be reliably classi ed as gene prediction errors. For 10. 8% of all mismatches, an alternative human isoform could be found in the Ensembl database corresponding to the primate sequence. In this case, the mismatch error was probably caused by the de nition of different canonical isoforms for human and primate sequences in the Uniprot database. Finally, 12. 7% of the mismatches were associated with at least one feature, but the cause of the error remains uncon rmed. These uncon rmed cases were mainly due to the detection of the mismatch in four or more primates (9. 6% of all mismatches), which suggests that either the same misprediction was propagated across multiple primates or the human protein sequence is in fact signi cantly different from the non-human primates and the mismatch is a false positive.
Discussion DNA protein sequence databases are increasingly useful research tools, but to maximize their potential, the errors in them need to be addressed. Contrary to expectations, advances in genome sequencing technologies have led to an increased number of protein sequence errors in public databases, including incomplete, incorrect or inconsistent sequences. Here, we used MSAs of closely related sequences to detect potential errors in primate proteins and investigate the underlying causes. We used a simple protocol based on BLASTP searches to identify potential orthologs of human proteins in ten primate proteomes. This method was chosen because it is time-e cient, convenient and allows identi cation of sequences in the most recent protein databases. More accurate tools for orthology detection have been developed, such as eggNOG  [31]  or OrthoInspector  [32].
However, these methods are computationally costly and are not always updated with each release of the Uniprot, Ensembl or RefSeq databases. We then chose to focus our analyses on the sequence errors involving mismatched segments, since mismatch errors can have a serious effect on downstream analyses. Mismatched segments generally arise when an exon is translated in the wrong reading frame, or the exon boundaries are badly predicted and a non-coding nucleotide sequence is translated instead. To ensure that the mismatched segments detected by our protocol were indeed caused by such errors in gene prediction, we eliminated the mismatch errors that were associated with the possible identi cation of primate paralogs instead of orthologs, or misalignments during the MSA construction process. This resulted in a total of 11,015 mismatch errors in Uniprot protein sequences, and 5971 mismatch errors in RefSeq proteins.
Funding This was supported by the  Institut Français de Bioinformatique (IFB) ,  ANR-11-INBS-0013 , the  ANR projects Elixir-Excelerate :  GA-676559  and  RAinRARE :  ANR-18-RAR3-0006-02 , and Institute funds from the  French Centre National de la Recherche Scienti que , the  University of Strasbourg.
On the Alink side, we identified a single connection between the A-link's trunk and A-tubule protofilament A8, an interaction originally described in C. reinhardtii  (Li et al, 2019)   (Fig EV5F and G). In addition to the A-C linker, we identified a large density between protofilaments A8 and A9 of the A-tubule that we termed the A-tusk (Figs  5B-D  and EV5C-E ). Interestingly, we observed that the triplet base connects to the A-C linker directly on the ArmB density (Figs  5A  and EV5D ), reinforcing our conclusion that the entire proximal region forms an interconnected structural network from the central hub of the cartwheel, through the radial spokes, the pinhead, and the triplet base to the A-C linker. To check whether the connection between the pinhead and the A-C linker is maintained throughout the proximal region, we split the dataset in two halves corresponding to the more proximal and more distal parts of this region  (Fig 5E and F and Appendix Fig S7).
Materials and Methods Paramecium tetraurelia centriole isolation and cryo-electron tomography P. tetraurelia cortical units were isolated from two different strains, the wild-type reference strain d4-2 and D-CenBP1, as previously described (Le  Guennec et al, 2020). Briefly, isolated P. tetraurelia centrioles were diluted with 1:1 colloidal gold in 10 mM K-PIPES buffer. Five microliters were deposited on 300 mesh lacey carbon grid and blotted from the backside before plunging in liquid ethane using a manual plunge freezing system. Tomograms were acquired with SerialEM software  (Mastronarde, 2005)  on a 300 kV FEI Titan Krios equipped with a Gatan K2 summit direct electron detector. The tilt series were recorded from approximately À60°to +60°(bidirectional, 2°steps, separated at À0°), using an object pixel size of 3.