dataesr/scientific-paragraphs-categorization huggingface dataset:
As a consequence, these databases comprised both the true protein sequences and a lot of false translated protein sequences as usually handled by proteogenomics  [4, 5]. To complete the search, two more databases, the Daphnia pulex whole-genome protein sequence database and the non-redundantd database NCBInr were used. In this case, the MS/MS spectra files acquired on the five biological replicates of the same species were merged before spectra assignation. The list of the overall assigned spectra and the peptide characteristics are described in Table  S1 , while the proteins identified are listed in Table  S2. Table  S3  summarizes the ratio of each database contribution in terms of spectra assignation. The 2192 identified proteins were then selected to create a specific ovary amphipod restricted database, which was named AMPHI-MERGE.
For the second step, spectra assignation of ovary proteome was performed with the AMPHI-MERGE database, for each of the 25 animal proteomes separately. The list of assigned spectra and the corresponding peptide characteristics are described in Table  S4  whereas the proteins identified and their spectral count quantitation are listed in Table  S5. Then, protein homologs were searched for the resulting identified proteins using the Blastp alignment tool. Homologous proteins were found for almost the entire protein list. Based on their most-closely homologs (same protein GeneID), the detected proteins were grouped together under one protein group. Finally, homolog proteins GeneID were used to associate a function to the detected proteins with the Gene Ontology annotation system. These data were used to define the core ovary proteome of the five amphipods  [1].
Protein sequence databases and MS/MS assignments For interpretation of MS/MS spectra, four databases were used. The National Center for Biotechnology Information nonredundant database (NCBInr) was downloaded on 2015/02/13. This version comprises 59,642,736 entries totaling 21,322,359,704 amino acids. The Daphnia pulex protein database, corresponding to the annotation of the whole genome shotgun sequence data ACJG00000000. 1 submitted to Genbank, was downloaded on 2015/02/25. This database comprises 30,611 entries totaling 10,015,651 amino acids. The GFOSS protein database, created from RNA-seq data acquired on G. fossarum, was as previously described  [2]. This database comprises 1,311,444 entries totaling 289,084,257 amino acids. The Parhyale database (PHAWA), obtained after sequencing of ovaries and embryo transcriptomes  [3] , was downloaded from the Harvard University resources on 2014/11/05.
The PHAWA database comprises 1,905,018 sequence entries totaling 277,367,091 amino acids. A restricted database containing all the proteins detected in a previous round of generalist database searches was created and named Amphi_Merge. It comprises 2192 protein sequences totaling 1,053,147 residues. Molecular ion peak lists were extracted with the Mascot Daemon software (version 2. 4. 0; Matrix Science) using the extract_msn. exe data import filter (Thermo). Data import filter options were set to 400 (minimum mass), 5000 (maximum mass), 0 (grouping tolerance), 0 (intermediate scans), and 1000 (threshold), as previously described  [10]. Peptide assignation with MASCOT was done with the following parameters: full trypsin specificity, maximum of two missed cleavages, mass tolerances of 5 ppm on the parent ion and 0.
In silico protein mining and functional annotation The protein sequences certified by MS/MS were used as queries to find the most similar sequences with the BLASTp module from the NCBI website facilities, as described previously  [2]. The NCBI gi number from the first NCBInr homolog (e-value threshold below 10) was used to merge amphipod protein groups leaving as main identifier the best MASCOT score hit from the AMPHI_MERGE database. For each of these main identifiers the first BLASTp NCBInr hit giving both an Entrez GeneID in gene2refseq and a GO correspondence in gene2go (ftp://ftp. ncbi. nlm. nih. gov/gene/DATA repository) was retrieved to build a matching GeneID list. Protein groups were then classified into GO categories by means of the Database for Annotation, Visualization and Integration Discovery (DAVID) based on the matching Entrez GeneIDs.
Acknowledgments We thank the  Institut National de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture (France) , the  Commissariat à l'Energie Atomique et aux Energies Alternatives (France)  through the  Transversal Toxicology Program (Pptox) , the  Agence Nationale de la Recherche program  " ProteoGam " ( ANR-14-CE21-0006-02 ) and the  Agence Nationale de la Recherche   CESA program  " GAMMA" 021 02  " Variability-Adaptation-Diversity and Ecotoxicology in Gammarids " (2012-2015) for financial support. The authors thank  Michalis Averof  ( IGFL, Lyon ) for his kind gift of P. hawaiensis animals and  Bernard Clément  ( ENTPE, Vaux en velin ) for generously supplying H. azteca animals.
Genome Sequencing and Assembly The genomes were sequenced using the Illumina MiSeq instrument (Illumina Inc. , San Diego, CA, USA) with the paired end application. The sequence reads were assembled de novo using ABYSS software  [23]  and the resulting contigs were ordered by the python-based CONTIGuator. py software  [24]. Concurrently, the CLC_Bio software  [25]  was used for mapping-based genome assembly using the APMV genome (NC_014649. 1) as reference. Draft genomes obtained by both strategies were mapped back to check the reads assembly and to close gaps. The best assemblages for each genome were kept, and the few remaining small gaps were closed by Sanger sequencing.
Genome Annotation The gene predictions were performed using RAST (Rapid Annotation using Subsystem Technology)  [26]  and GeneMarkS  [27]  tools. Transfer RNA (tRNA) sequences were identified using the tRNAscan-SE tool  [28]. The functional annotations were inferred by BLAST searches against the GenBank NCBI non-redundant protein sequence database (nr) (e-value < 1 × 10 -3 ), the set of clusters of orthologous groups of proteins (COGs) of the NCLDVs (named NCVOGs)  [16]  and by searching specialized databases through the Blast2GO platform  [29]. Finally, the genome annotation was manually revised and curated. The predicted ORFs that were smaller than 100 amino acids and had no hit in any database were ruled out. The ORFs longer than 100 amino acids without hits in any database (ORFans) were kept.
Analysis of the Pan-Genome and Core Genome of Brazilian Mimiviruses To estimate the size of the pan-genome of Brazilian mimiviruses, their predicted proteins were clustered by the BLASTclust program using an amino acid sequence identity of 30% and sequence coverage of 70% as the parameters. We also described the pan-genome size evolution by stepwise inclusion of each new virus annotation in the pairwise comparisons of the gene contents. The orthologous protein clusters encompassing at least one protein from each virus, obtained by the BLASTclust and Proteinortho5. pl programs, were taken into account to determine the core genome and strict core genome, respectively, of the mimivirus lineage A group.
Genome Annotation of Brazilian Mimiviruses The genomes of AMAV (GenBank Accession no. KM982403), KROV (KM982402) and OYTV (KM982401) are double-stranded DNA molecules of 1,179,579, 1,221,932 and 1,200,220 bp, respectively. KROV presents the largest DNA genome among lineage A amoebal mimiviruses. Its genome is ∼21, 42 and 40 kb larger than those from OYTV, AMAV and SMBV, respectively, and ∼30 kb and 40 kb larger than those from Mamavirus and APMV, respectively. These genomes have a mean G+C content of 27%, similar to other mimiviruses. The genomes of AMAV and OYTV contain six regions encoding the tRNA molecules for leucine (3 sequences), histidine, cysteine and tryptophan amino acids, whereas the genome of KROV lacks the sequence encoding the tRNA for tryptophan. The assessment of tRNA gene expression was congruent with the genomic analyses, by detecting six tRNA molecules in SMBV and OYTV, whereas no tryptophanyl-tRNA expression was detected in KROV.
3489 The functional annotation revealed that ∼50% of the ORFs in Brazilian mimivirus genomes were hypothetical proteins, i. e. , without a defined function. For all the Brazilian mimivirus genomes, the best hits were most frequently proteins from APMV, followed by those from Mamavirus (Figure  2 ). These viruses belong to lineage A of the amoebal mimiviruses. One ORF in the AMAV genome, three ORFs in the KROV genome and three ORFs in the OYTV genome had no hit against the NCBI nr protein sequence database or the Blast2GO platform. To confirm whether these genes were bona fide ORFans, their nucleotide sequences were submitted to a BLASTn search against the GenBank nucleotide sequence database (nt) of the NCBI. For AMAV, the putative ORFan R931 had hits with other lineage A mimiviruses (coverage of 49%; similarity of 100%; e-value of 1 × 10 -20 or less).
In contrast, the three putative KROV ORFans (L22, L61 and R933 genes) had no significant hits. Transcripts for these three ORFans were detected by Sanger sequencing. For OYTV's putative ORFans, significant hits against lineage A mimiviruses were only obtained for R123 and L733 genes. For ORFan L25, a hit with an e-value of 0. 63, coverage of 83% and identity of 26% was observed against Lausannevirus, a giant amoebal virus from the family Marseilleviridae. Whether these putative ORFans were shared by Brazilian mimiviruses was determined by BLASTn analyses. The putative ORFan of AMAV was shared by the KROV and OYTV genomes, whereas ORFans from KROV were not found in any other Brazilian mimivirus genomes. The OYTV putative ORFans L733 and R123 were shared by KROV and AMAV, whereas ORFan L25 was exclusive to the OYTV genome.
Among the predicted ORFs, 45% were hypothetical proteins, and 85% showed a best hit against APMV. We identified four sequences encoding aminoacyl-tRNA synthetase, a landmark of mimiviruses because no other virus encodes such proteins. Moreover, we were able to identify six tRNA sequences: three cognates to leucine, and one to histidine, cysteine or tryptophan. The G+C content of these tRNA sequences was 48. 6%, far greater than that for the entire genome. All the predicted ORFs matched orthologs in the NVCOG database with an average similarity of 93. 7%; thus, new ORFans were not predicted. A total of 58 clusters consisting of 179 paralogous proteins were identified in the SMBV genome, similar to that detected in the APMV and Hirudovirus genomes. The reciprocal best hit analysis identified 917 bona fide orthologous proteins between SMBV and APMV.
Comparative Genomics of Lineage A Mimiviruses The genome sequence of SMBV was most closely related to APMV, with a similarity level of 99% (and a coverage of 100%), followed by AMAV (similarity: 99%, coverage: 99%), OYTV (similarity: 99%, coverage: 97%) and KROV (similarity: 94%, coverage: 90%). The same tendency was observed among Brazilian mimiviruses and other lineage A mimiviruses, such as Mamavirus (JF801956. 1), Terra2 virus (NC_023639. 1) and Hirudovirus (KF493731. 1) (data not shown). Among the mimi-BR, SMBV, AMAV and OYTV genomes had similarity values ranging from 97% to 99%, whereas KROV had similarity values between 90%-91% with the other mimi-BR. The gene synteny analysis showed that mimi-BR genomes share a similar architecture with those from the other lineage A mimiviruses, with some reassortments at their extremities, primarily The Acanthamoeba polyphaga mimivirus was the predominant target for hits from all Brazilian mimiviruses (896, 588 and 577 best hits to AMAV, OYTV and KROV, respectively), with an average of 76% of the hits.
Comparative Genomics of Lineage A Mimiviruses The genome sequence of SMBV was most closely related to APMV, with a similarity level of 99% (and a coverage of 100%), followed by AMAV (similarity: 99%, coverage: 99%), OYTV (similarity: 99%, coverage: 97%) and KROV (similarity: 94%, coverage: 90%). The same tendency was observed among Brazilian mimiviruses and other lineage A mimiviruses, such as Mamavirus (JF801956. 1), Terra2 virus (NC_023639. 1) and Hirudovirus (KF493731. 1) (data not shown). Among the mimi-BR, SMBV, AMAV and OYTV genomes had similarity values ranging from 97% to 99%, whereas KROV had similarity values between 90%-91% with the other mimi-BR. The gene synteny analysis showed that mimi-BR genomes share a similar architecture with those from the other lineage A mimiviruses, with some reassortments at their extremities, primarily compared to the Terra2 virus and Hirudovirus (Figure  3 ).
In particular, we studied the gene arsenal and genome architecture of these viruses and their relationship with other members of this viral family. As for other mimivirus genomes, the majority of the ORFs in Brazilian mimivirus genomes were ORFans or putative genes encoding hypothetical proteins, and their annotation was based on similarity with previously described mimiviral genomes. Thus, most of these ORFans are family ORFans, which means that they have no known ortholog in public sequence databases apart from other mimiviral genomes  [39]. Hence, the function, significance and evolutionary relationship remain to be deciphered for a significant proportion of putative genes in these mimiviral genomes. As previously described  [8] , the SMBV genome is closely related to APMV and has high similarity with other Brazilian mimiviruses such as OYTV and AMAV.
Acknowledgments We thank our colleagues from  Gepvig and Laboratório de Vírus of Universidade Federal de Minas Gerais  and the bioinformatics team of  URMITE-Aix Marseille Université  for their excellent technical Viruses 2015, 7 support. We would also like to thank  CNPq ,  CAPES ,  FAPEMIG and Pro-Reitoria de Pesquisa da Universidade Federal de Minas Gerais (PRPq-UFMG)  for financial support.
Within each community i, the probability p s,i for an individual to belong to species s is estimated by ps,i = n s,i /n i. The same probability for the meta-community is p s. Communities have a weight w i , satisfying p s = i w i p s,i. The commonly used w i = n i /n is a possible weight, but the weighting may be arbitrary (e. g. , depending on the sampled areas). The component Ps of a 'MetaCommunity' object contains the probability of occurrence of each species in the meta-community, calculated in this way: A 'MetaCommunity' object can be summarized and plotted (Figure  1 ). The package contains an example dataset containing the inventory of two 1-ha tropical forest plots in Paracou, French Guiana  (Marcon, Hérault, Baraloto, and   Paracou618. MC is a meta-community made of two communities named "P006" and "P018", containing 425 species (their name is Family_Genus_Species, abbreviated to 4 characters).
The values of the abundance matrix are the number of individuals of each species in each community. Sample coverage will be explained later. The dataset also contains a taxonomy and a functional tree. Paracou618. Taxonomy is an object of class 'phylog', defined in package ade4  (Dray and Dufour 2007) , namely a phylogenetic tree. This example data is only a taxonomy, containing family, genus and species levels for the sake of simplicity. Paracou618. Functional is an object of class 'hclust' containing a functional tree based on leaf, height, stem and seed functional traits  (Hérault and Honnay 2007; Marcon and Hérault 2015a). The package accepts any ultrametric tree of class 'phylog' or 'hclust'. Paracou618. dist is the distance matrix (actually a 'dist' object) used to build the functional tree.
Its interpretation is straightforward: Some species have not been sampled. Their number is unknown but their total probability of occurrence can be estimated accurately. Here, it is a bit less than 8%. From another point of view, the probability for an individual of the community to belong to a sampled species is C: 8% of them belong to missed species. If the number of missed species are of interest, they can be estimated using other software packages (e. g. , the R package SPECIES, Wang 2011), but we will not discuss this in detail here. The sample coverage is the foundation of many estimators of entropy.
Effective numbers of species Entropy should be converted into "true diversity"  (Jost 2007) , i. e. , effective number of species equal to  Hill (1973)  numbers: q D = s p q s 1 1-q. (5) This can be done by the deformed exponential function, or using directly the Diversity or bcDiversity functions (equal to the deformed exponential of order q of Tsallis or bcTsallis) R> expq(Simpson(Ps = Paracou618. MC$Ps), q = 2) [1] 68. 7215 R> Diversity(Ps = Paracou618. MC$Ps, q = 2) [1] 68. 7215 R> expq(bcTsallis(Ns = Paracou618. MC$Ns, q = 2), q = 2) [1] 73. 19676 R> bcDiversity(Ns = Paracou618. MC$Ns, q = 2) [1] 73. 19676 The effective number of species of the Paracou dataset is estimated to be 73 after bias correction (rather than 69 without it). It means that a community made of 73 equally-frequent species has the same Simpson entropy as the actual one.
Taxonomy Diversity is normalized Diversity equals: 55. 13383 The phylogenetic diversity of order 1 of the Paracou dataset is 55 effective species: 55 totally different species (only connected by the root of the tree) with equal probabilities would have the same entropy. It can be compared to its species-neutral diversity, 134 species. The latter is the diversity of the first slice of the tree. When going up the tree, diversity decreases because species collapse. On Figure  6 , diversity of the second slice, between T = 1 and T = 2, is that of genera (64 effective genera) and the last slice contains (20 effective families). The phylogenetic entropy of the community is the average of the entropy along slices, weighted by the slice lengths. Diversity cannot be averaged in the same way. A less trivial phylogeny would contain many slices, resulting in as many diversity levels with respect to T.
Conclusion The entropart package allows estimating biodiversity according to the framework based on HCDT entropy, the correction of its estimation bias  (Grassberger 1988; Chao and Shen 2003)  and its transformation into equivalent numbers of species  (Hill 1973; Jost 2006; Marcon et al. 2014a). Phylogenetic or functional diversity  (Marcon and Hérault 2015a ) can be estimated, considering phyloentropy as the average species-neutral diversity over slices of a phylogenetic or functional tree  (Pavoine et al. 2009). Similarity-based diversity  (Leinster and Cobbold 2012)  can be used to estimate  (Marcon et al. 2014b ) functional diversity from a similarity or dissimilarity matrix between species without requiring building a dendrogram and thus preserving the topology of species  (Pavoine, Ollier, and Dufour 2005; Podani and Schmera 2007).
The classical diversity estimators (Shannon and Simpson entropy) can be found in many R packages. Package vegetarian (Charney and Record 2012) allows calculating Hill numbers and partitioning them according to Jost's framework. Bias correction is never available except in the package EntropyEstimation  (Cao and Grabchak 2015) , which provides the Zhang and Grabchak's estimators of entropy and diversity and their asymptotic variances (not included in package entropart). Phylodiversity and similarity-based diversity are not available in any package as far as we know. So we believe entropart is a useful toolbox for ecologists who need to estimate the diversity of actual, undersampled communities and to partition it.
This function is used to calculate diversity or entropy profiles based on community functions such as Tsallis or ChaoPD. It is similar to DivProfile but does not require a 'MetaCommunity' as argument. It returns a list which can be plotted. This example evaluates bias correction on the diversity profile of the Paracou dataset. First, diversity profiles are calculated with and without bias correction: R> bcProfile <-CommunityProfile(bcDiversity, Paracou618. MC$Ns) R> Profile <-CommunityProfile(Diversity, Paracou618. MC$Ps).
, 2009]  to assess the impacts of overlapping clouds on the MODIS retrieval failure rates. The collocation is done by matching CALIOP profiles to the nearest MODIS pixels by minimizing the distance between their footprints on the surface  [Cho et al. , 2008]. This simple collocation algorithm does not account for the parallax effect caused by the difference in observing geometry between CALIOP and MODIS. But we compared our collocation scheme with a more rigorous one developed by  [Holz et al. , 2008]  which accounts for the parallax effect using cloud height retrievals from CALIOP. The comparison results based on 1 month of data confirms that because most MLP clouds are low the parallax effect has a negligible impact on our failure retrieval analysis. The impacts of cloud phase classification errors are assessed using CALIOP's cloud layer phase retrieval  [Hu et al.
Acknowledgments This research is supported by  NASA  grants  NNX11AI98G  and  NNX14AJ25G  managed by  Richard Eckman. Zhibo Zhang  would like to thank  Gala Wind ,  Thomas Arnold , and  Nandana Amarasinghe  for their help on MODIS C6 data. The computations in this study were performed on  UMBC High Performance Computing Facility (HPCF). The facility is supported by the  U. S. National Science Foundation  through the  MRI program  (grants  CNS-0821258  and  CNS-1228778 ) and the  SCREMS program  (grant  DMS-0821311 ), with additional substantial support from  UMBC. The MODIS data are obtained from  NASA 's Level 1 and  Atmosphere Archive  and Distribution System (LAADS http://ladsweb. nascom. nasa. gov/). The CALIOP data are obtained from  NASA' Atmospheric Science Data Center (ASDC , https://eosweb.
In Sect. 3 of Part 1, we present a survey of the Airbus measurements and an analysis of the highest IWC conditions encountered in all three regions at cruise altitudes around 10-12 km and -40 to -50 • C. These cold temperatures were the focus of the Airbus flight tests, in part because over one-third of the engine events reported by  Grzych and Mason (2010)  occurred at temperatures colder than -35 • C (and over a quarter at temperatures colder than -40 • C). The importance of such cold temperatures is further supported by the latest Boeing engine icing event database of 162 events occurring at a median temperature of -36 • C  (Bravin et al. , 2015). IWC derived from the Robust probe measurements and integration of the nephelometer size distributions generally agree to about 25 % over a wide range of IWC; uncertainty in each is estimated to be roughly a factor of 2 owing in large part to the uncertainty in Robust probe calibration  (Grandin et al.
Acknowledgements. TWP-ICE soundings and S-band radar data were obtained from the  Atmospheric Radiation Measurement (ARM) Program  sponsored by the  U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Climate and Environmental Sciences Division. C-POL radar measurements and retrieval products were supplied by  Peter May ,  Centre  for  Australian Weather  and  Climate Research ,  Australian Bureau of Meteorology. The authors thank  Thomas Ratvasky  and  Renato Colantonio  for logistical and programmatic assistance, and  Jeanne Mason ,  Matthew Grzych ,  Alain Protat , and  Alfons Schwarzenböck  for valuable discussions and  Airbus  for providing their flight-test measurements. This work was supported by the  NASA   Aviation Safety Program's Atmospheric Environment Safety Technologies Project.