dataesr/scientific-paragraphs-categorization huggingface dataset:
Base pair call errors were corrected in three iterations of ICORN  (Otto et al. , 2010) , using the amplification-free library. Furthermore, sequencing gaps were closed, using IMAGE  (Tsai et al. , 2010)  with the amplification-free library. The assembly was quality-controlled using REAPR  (Hunt et al. , 2013) , breaking the contigs at possible miss-assemblies, using the mate pair libraries. This was followed by another scaffolding step. We systematically removed 620 scaffolds containing 25. 65 Mb representing the bacterial contamination. The Vitrella CCMP3155 assembly contains 72. 7 Mb (including 931,689 N's) in 1064 scaffolds (ENA accession numbers CDMY01000001-CDMY01001064). The scaffolds were constructed from 4177 contigs. For Chromera, the assembly pipeline and the algorithms used were the same as Vitrella, but due to the larger size, higher amount of low-complexity regions, and difficulties in generating high-quality large insert size libraries, additional steps were included to the assembly process.
First, the reads of the PCR-Free library were corrected with SGA  (Simpson and Durbin, 2012 ) and then assembled with velvet and using a k-mer of 71 (version August 2011). Next, the contigs were scaffolded, gapfilled, and corrected with ICORN, as described earlier. We mapped the reads of all large insert size libraries using SMALT (ftp://ftp. sanger. ac. uk/pub/resources/software/smalt/). We excluded scaffolds smaller than 1 kb. Different iterations with SSPACE were undertaken and the assembly was quality-checked with REAPR. After scaffolding, gapfiller and IMAGE were run as above, followed by ICORN. The 1725 scaffolds (spanning 16. 02 Mb) representing bacterial contamination were removed. The final assembly of Chromera CCMP2878 contains 193. 66 Mb (including 582,995 N's) in 5953 scaffolds (ENA accession numbers CDMZ01000001-CDMZ01005953).
Gene prediction We used Augustus  (Stanke et al. , 2006 ) (version 2. 5. 5) for gene prediction. We manually curated 716 and 245 gene models for Chromera and Vitrella, respectively, using BLAST similarity-based approaches, and we also generated automated gene models using Cufflinks  (Trapnell et al. , 2012)  from RNA-seq data sets, in order to use them as a 'training gene model set' for Augustus prediction. The strand-specific RNA-Seq, mapped with TopHat2  (Kim et al. , 2013) , was used as evidence in Augustus for intron evidence. In summary, from the Chromera and Vitrella genome, we ab initio predicted 30,478 and 23,503 protein-coding genes, respectively, of which 18,829 and 18,240 were detected as being expressed from RNA-seq evidence as poly A+ transcripts (Supplementary file 1). Excluding putative TEs, 26,112 and 22,817 genes were predicted as protein-coding genes in Chromera and Vitrella.
We annotated partial genes, when a gene probably spans more than one scaffold, located at the borders of a scaffold. We demarcated and annotated as pseudo genes if they contain in frame stop codons. We flagged gene models as transposon elements, if they overlap with the predicted TE regions and had no more than three and two intron for Chromera and Vitrella, respectively. To annotate untranslated regions (UTRs) of the predicted protein-coding genes, we used CRAIG  (Bernal et al. , 2007)  with default parameters with mapping of the RNA-Seq data as computed by GSNAP  (Wu and Nacu, 2010)  (version 2013-08-19, default parameters). The annotation of both genomes has the ENA accession numbers CDMZ01000001-CDMZ01005953 and CDMY01000001-CDMY01001064 and is also available in EuPathDB  (Aurrecoechea et al.
Functional annotations The predicted genes were assigned putative functions based on BLASTP (E value <10 -6 ) matches against UNIPROT (version March 2012). The predicted protein products were assigned protein domains using hmmsearch (HMMER 3. 1b1, May 2013) for Pfam A v26. 0. Statistical threshold defined by the Pfam  (Finn et al. , 2014)  database was used. We aligned AP2 sequences in apicomplexan species based on PfamA AP2 (PF00847), and built apicomplexan-specific AP2 (apiAP2) hidden Markov model (HMM), and scanned the predicted protein-coding genes for apiAP2 domains; we annotated api-AP2 DNA-binding transcription factor genes with both domain and sequence E values to be less than 10 -3. The following Pfam RBDs were used to define RNA-binding proteins: 'CAT_RBD', 'dsRNA_bind', 'S1', 'DEAD', 'KH_1', 'KH_2', 'KH_3', 'KH_4', 'KH_5', 'RRM_1', 'RRM_2', 'RRM_3', 'RRM_4', 'RRM_5', 'RRM_6', 'SET', 'PUF', and 'RAP'.
The list of DBDs was downloaded from a database of DBDs  (Wilson et al. , 2008). Transmembrane domains and signal peptides were assigned with the tools TMHMM 2. 0  (Krogh et al. , 2001)  and signalP 4. 0  (Petersen et al. , 2011) , respectively, with default parameters. We collected several categories of genes implicated in parasitic processes in apicomplexans for two archetypal apicomplexan parasites, Toxoplasma and Plasmodium. We primarily obtained annotations from PlasmoDB  (Bahl et al. , 2003)  and ToxoDB  (Gajria et al. , 2008). Information for sub-cellular localization of genes is obtained from GeneDB  (Logan-Klumpler et al. , 2012)  and ApiLoc, a database of published protein sub-cellular localization for apicomplexan species (http://apiloc. biochem. unimelb. edu. au/apiloc/apiloc).
Evolutionary analysis We compiled the reference proteomes of 26 alveolate and stramenopile species (Figure  1 -source data 2) from public databases such as EupathDB  (Aurrecoechea et al. , 2013)  and NCBI Genome database (http://www. ncbi. nlm. nih. gov/genome/). We generated a phylogenetic species tree using a data set composed of 101 one-to-one orthologs across the 26 species (see Figure  1 -source data 1 for gene IDs). Amino acid sequences were aligned using MAFFT  (Katoh and Standley, 2013) , highly variable sites were edited by trimAL  (Capella-Gutierrez et al. , 2009)  and after manual inspection. The resulting alignment of 33,997 amino acid positions was used to construct trees by a maximum likelihood (ML) method and Bayesian inference. The ML tree was computed using RAxML 8. 1. 16 by gamma corrected LG4X model  (Stamatakis, 2014; Le et al.
, 2012). Robustness of the tree was estimated by bootstrap analysis in 1000 replicates. Bayesian tree was constructed by PhyloBayes  (Lartillot and Philippe, 2004)  using two-infinite mixture model CAT-GTR as implemented in PhyloBayes 3. 3f. Two independent chains were run until they converged (i. e. , maximum observed discrepancy was lower than 0. 2), and the effective number of model parameters was at least 100 after the first 1/5 generation was omitted from topology and posterior probability inference. All clades in the tree were supported with posterior probability 1. 00 and 100% bootstraps, except for one node, which representing the common ancestor of human Plasmodium spp. was supported by 99% bootstrap. We performed the gene gain and loss analysis based on Dollo parsimony using Count software  (Csuros, 2010).
Major datasets The following datasets were generated: (RAxML  [Stamatakis, 2014] ), Bayesian inference (PHYLOBAYES  [Lartillot and Philippe, 2004] ), and a method designed to deal with amino acid saturation (AsaturA [  Van de Peer et al. , 2002] ). ML trees were computed under the gamma corrected LG4X model of evolution as implemented in RAxML 7. 4. 8a using the rapid-bootstrap optimization algorithm in 1000 replicates. Bayesian phylogeny was inferred using empirical site-heterogenous model C40 as implemented in Phylobayes 3. 2f. Two independent chains were run until they converged (i. e. , maximum observed discrepancy was lower than 0. 2), and the effective number of model parameters was at least 100 after the first 1/5 generation was omitted from topology and posterior probability inference.
Author( AsaturA trees were computed using a Poisson corrected LG model and the support was assessed from 1000 replicates. Sequences from Vitrella (all enzymes under investigation) and Chromera (CPS and FAS enzymes) were inspected for the presence of N-terminal leader sequences using SignalP  (Bendtsen et al. , 2004)  and TargetP  (Emanuelsson et al. , 2007)  software respectively, suggesting targeting to either mitochondrion (with mitochondrial transit peptide) or plastid (with bipartite leader composed of ER signal peptide and transit peptide).
Global metabolic map Metabolic annotations based on ortholog assignments with KEGG and OrthoMCL database showed that chromerids contain all major primary metabolic pathways typically found in freeliving unicellular eukaryotes (Figure  2 -figure supplement 3). 2918 Chromera proteins and 2985 Vitrella proteins were assigned KO numbers, from which 432 Chromera (1. 3% of proteome) and 425 Vitrella proteins (1. 8% of proteome), respectively, were identified as enzymes with a metabolic function based on EC number association. In support of their autotrophic lifestyle, the chromerids appear to be capable of generating de novo all primary carbon metabolites such as the various sugars and other reduced carbon compounds (presumably via photosynthesis and associated carbon fixation pathways), amino acids, nucleotides, fatty acids and lipids, isoprene and steroid derivatives, and most vitamins (except biotin and vitamin B12).
gondii only appear to encode the CORVET-specific interactor Vps3. Chromera and Vitrella, like Apicomplexa, possess the entire VpsC core complements as well as the HOPS component Vps41 and both CORVET components. Chromerids exhibit complex life cycles, from immotile vegetative cells to multi-cellular sporangia, and occasionally motile flagellated cells. Both lineages contain numerous potential locales for intracellular trafficking including mitochondria, plastid, starch granules, flagella, micronemes, and, in Chromera, the chromerosome. Additionally, vesicular traffic to the sporangial/cyst wall has been visualized in both lineages  (Oborník et al. , 2012). Our results indicate that chromerids possess an appropriately complex complement of membrane trafficking machinery to achieve these requirements.
On the other hand, we detected the most complete myosin family repertoire in Chromera and Vitrella. We detected certain myosin families in some apicomplexan genera, but not among nonapicomplexan species, indicating lineage-specific gains (data not shown). In summary, combinations of lineage-specific losses and gains have led to streamlined, unique repertoires of actins and myosins in various apicomplexan species. Appendix 6 ApiAP2 proteins. We examined the abundance of apicomplexan-specific AP2 (apiAP2) genes, transcription factors that play regulatory roles in key aspects of apicomplexan biology  (Campbell et al. , 2010; Flueck et al. , 2010; Radke et al. , 2013; Kafsack et al. , 2014; Sinha et al. , 2014). We scanned the protein-coding gene sets of the 26 species using the apicomplexan-specific apiAP2 HMM, which was constructed with the AP2 domain sequences from apicomplexan species.
, 2001)  and due to higher rate of sequence divergence in parasites  (Hafner et al. , 1994). Previous studies have shown that plant genomes contain a large repertoire of AP2 genes, and that plant AP2 domains evolved from an endonuclease domain in a cyanobacteria  (Magnani et al. , 2004). According to our phylogenetic analysis, AP domains among bacteria are many and diverse, with both plant-like and apicomplexan-like AP2s (data not shown). We did not find significant homology with bacterial AP2 genes at the full gene length level. It is not clear if the originally transferred AP2 gene has evolved beyond recognition or if only the domain has been transferred to these eukaryotes. The exact genetic events that led to acquisition of AP2s in apicomplexans are not clear. However, what is the most probable scenario is that AP2 domains in alveolates came from bacteria and have expanded in myzozoans, independent of those in plants.
Acknowledgements We thank the  KAUST Bioscience Core Laboratory  personnel for sequencing specific Illumina libraries used in this project,  KAUST Computational Bioscience Research Center  for providing computing resources,  Gordon Langsley  ( Institut Cochin, Inserm U1016, Paris ) and  Anthony Holder  ( The Francis Crick Institute, London ) for comments on the manuscript draft. AV and CB thank  Florian Maumus  for advice regarding TE annotation. The primary funding for this work was provided by  KAUST  award  FIC/ 2010/09  to JL, MO, and AP.
This suggests that the common ancestor of Myzozoa (chromerids, apicomplexans, and dinoflagellates) had all the subunits, and they were lost in different lineages. Genes encoding ALP1, hypothesized to function as Arp2/3-like nucleator  (Gordon and Sibley, 2005) , were found in apicomplexans and also in Vitrella (Vbra_266. t1). FH2-domain (Pfam-PF02181) containing formins are members of another actin nucleator gene family. They produce unbranched filaments unlike Arp2/3 complex, which induces branched filaments. Both chromerids possess formin1 (FRM1) and formin 2 (FRM2) homologs, which are conserved in all the other studied species as well. Although Plasmodium spp. maintained a 1-1 orthology for both FRM1 and FRM2, we found a coccidian-specific FRM3 (TGME49_213370), suggesting a lineage-specific expansion.
Ultrastructures reminiscent of micronemes have been observed in both chromerids  (Oborník et al. , 2012) ; consistent with this, we identified EC proteins having domains and architectures typical of Toxoplasma and Plasmodium micronemal secretory proteins. Examples include expansions of proteins containing SUSHI, EGF, TSP1, and vWA domains (data not shown). Chromerids possess unique architectures of proteins containing the macrophage perforin (MacPerf) domain (Figure  3 -figure supplement 4E), which, previously found in apicomplexans and ciliates (as large expansions), are thought to function in apicomplexans to mediate membrane lysis during host cell egress and tissue traversal  (Roiko and Carruthers, 2009). The Chromera MacPerf domain proteins also contain arrays of a domain, WSC, thus far not found in other alveolates, as well as a unique C-terminal DERM domain.
The real-time aspect is an important factor for Wireless Sensor Network (WSN) applications, which demand a timely data delivery, to reflect the current state of the environment. However, most existing works in the field of sensor networks are only interested in data processing and energy consumption, to improve the lifetime of WSN. Extensive researches have been done on data processing in WSN. Some works have been interested in improving data collection methods, and others have focused on various query processing techniques. In recent years, the query processing using abstract database technology like Cougar and TinyDB have shown efficient results in many researches. This paper presents a study of timing properties through two data processing techniques for WSN. The first is based on a warehousing approach, in which data are collected and stored on a remote database.
INTRODUCTION Wireless sensor networks can be considered as a type of ad hoc wireless networks. They are composed of small wireless nodes, which are manually or randomly deployed in a region of interest, to sense different physical characteristics of the environment, like temperature, humidity, pressure, light, and so on. The nodes transmit the sensed data to a sink node referred to as Base Station (BS). This process, called data collection, must be able to meet certain deadlines, i. e. to update data before the expiration of their validity intervals, in order for the WSN reflects the current state of the environment. WSN applications, such as industrial process monitoring and control, can be considered as timecritical. They require strict deadlines to send data to the sink nodes. WSN are often deployed without giving much importance to the temporal constraints including the data freshness constraint and the deadline constraints.
Data freshness constraint imposes a limit on the data validity period from the time it is read from a sensor. The constraint of data deadline also requires that data have to be collected within a deadline  (Bhattacharya, 2008). In real-time applications, data freshness is a key performance factor. It is important to properly study the efficiency of data management mechanisms in WSN. Nowadays, researchers are interested in data processing techniques to offer an efficient solution to increase the WSN lifetime. They have proposed new techniques for data aggregation, data collection and query processing, to simplify the data extraction and management in WSN. The query processing systems such as Cougar  (Fung et al. , 2002)  and TinyDB  (Madden et al. , 2003)  use the abstract database approach which considers the WSN as a relational database.
They aim to improve the data extraction mechanisms in WSN to save energy and to reduce the communication losses. (García-Hernando et al. , 2008). In this paper, we are interested in investigating two approaches for data processing in WSN (i) a query processing approach using an abstract database and (ii) a periodic data collection. We simulate temporal constraint in order to identify which method gives better data arrival time to the user and that ensures data freshness in WSN. We consider the following performance parameters: (i) the network convergence time, which is the time that a sensor node takes to connect to the base station, to build and to update its routing tables. It can affect the arrival time of data, (ii) the data collection time. The fastest is this parameter, the more fresh are data, (iii) the database response time, which can improve the data arrival time to the user.
RELATED WORK In this section, we give an overview of some research works, which have studied the time criterion for WSN data processing. Thereafter, we present the different abstract databases for sensor networks. (Sharaf et al. , 2003). TiNA exploits temporal coherency tolerance, to reduce the number of transmitted messages by each node. The authors have proposed a new clause named "tct" (temporal coherency tolerance) for each query, in order to express temporal coherency tolerance. For example, the following query involves getting the total light for rooms and grouping by floor, with tct = 10%. The new reading is not reported if the difference between it and old one is smaller than the associated tct value.
Abstract Database for WSN Abstract databases for WSN have been cited for the first time by  Bonnet et al. (Bonnet et al. , 2001). They consider the WSN as a relational database that can be queried using SQL-like queries. Abstract databases present a declarative approach to facilitate the description and processing of queries in the WSNs. Data required by the base station form a virtual table, in which columns rerepresent the data requested by the user. Madden et al. have presented a new query processing system, named TinyDB, using TinyOS  (Levis et al. , 2005 ) as an operating system, for extracting information from a network. It is composed of two subsystems: (i) TinyDB application, running on each node of the network, (ii) Java-based client interface, allowing the user to describe the data using a declarative SQL-LIKE queries, i.
e. , without knowing how the data are processed in network. The users writes the query that will be compiled and split into tasks, then injected into the network. Once a node receives the query, it processes it, and returns the results to the user via the base station. For example, to calculate the average temperature for all sensors when the light is greater than 400 for a period of 10 seconds, we write the following query: SELECT AVG (temperature) FROM sensors WHERE light > 400 EPOCH DURATION 10s TikiriDB  (Laxaman et al. , 2010 ) is a database abstraction layer for ContikiOS. It provides function-.
SELECT light,temperature FROM sensors WHERE temperature > 50 SAMPLE PERIOD 2s FOR 10s Amato et al. have proposed MaD-WiSe (MAnagement of Data in WIreless SEnsor networks)  (Amato et al. , 2010) , which considers a WSN as a highly distributed and dynamic database. MaD-WiSe takes account of various aspects related to database system design. Users can express queries using an SQL-like language, named MW-SQL, to collect, to filter, and to manage sensors data. Corona  (Khoury et al. , 2010 ) is a distributed innetwork query processing system which provides a declarative query language SQL-Like to formulate queries. Corona introduces the notion of freshness into WSN, allowing the user to obtain data from a sensor network with data freshness guarantees. It uses multiple aggregate queries to reduce processing delays and costly communication in WSN.
SELECT temperature FROM sensors WHERE light > 100 EPOCH 60s FRESHNESS 10s In this example, the user queries all sensors to get the temperature greater than 50 for epoch period of 60 seconds. It defines also the freshness constraint clause of 10 seconds to fix the time allowed between two acquisitions. We note that temporal constraints have not been considered in the most of abstract databases. They have been studied only in the recent Corona work  (Khoury et al. , 2010). In the next section, we illustrate our network model through two scenarios: a periodic data collection and a query processing using TinyDB and we present the parameters we selected to discuss them in section 4.
NETWORK MODEL In the two scenarios, we have used a network model composed of four components: (i) sensor nodes which report informations about the environment, (ii) a base station which collects periodically data from sensor nodes, (iii) a remote database used to store the received data, (iv) a user which can connect to the database and get information about WSN. In the second scenario, we have added an abstract database for WSN, named TinyDB, which allows the user to send queries to the base station and to get queries responses.
First Scenario: Data Collection with Remote Database In this scenario, sensor nodes are randomly deployed over an area and the base station is placed outside the network. Nodes communicate amongst themselves through wireless communications. Sensor nodes retrieve data values like temperature and humidity, and send them to the base station. The data received are transmitted to the remote database to be stored. Finally, they can be displayed through an interface to the users. them to the base station via the the abstract database interface. The base station will inject these queries over the network. Each sensor receives a query, responds with the values requested for a duration fixed in the query by the user. The base station sends the responses to the user via the abstract database system and they can be stored in a remote database.
We have considered some performance parameters for the two approaches and we discussed them in next section. These parameters are: (i) the network convergence time, an important factor to ensure the temporal constraints in WSN. It precedes the process of data collection. It can affect on the arrival time of data when it takes a long of time. We consider also (ii) the data collection time, which presents an important factor to guarantee the freshness of data and the data delivery deadline. When the data collection is fast, data freshness will be better. Finlay, (iii) the database response time which can be considered as an important factor in the first scenario, because it represents the only data available to the user. We will show in the next section (simulations) that sometimes, data freshness cannot be met in this stage.