dataesr/scientific-paragraphs-categorization huggingface dataset:
Diatoms are one of the most successful and ecologically important groups of eukaryotic phytoplankton in the modern ocean. Deciphering their genomes is a key step towards better understanding of their biological innovations, evolutionary origins, and ecological underpinnings. Here, we have used 90 RNA-Seq datasets from different growth conditions combined with published expressed sequence tags and protein sequences from multiple taxa to explore the genome of the model diatom Phaeodactylum tricornutum, and introduce 1,489 novel genes. The new annotation additionally permitted the discovery for the first time of extensive alternative splicing (AS) in diatoms, including intron retention and exon skipping which increases the diversity of transcripts to regulate gene expression in response to nutrient limitations.
3) Modified gene models, genes whose structural annotation has a different 5' end, 3'end or both 5' and 3' ends with respect to Phatr2. Thirty of these genes with a different N terminus in Phatr3 compared to Phatr2 were validated by their presence within a previously constructed multi-gene reference dataset of aligned plastid-targeted proteins that are well conserved across ochrophyte lineages (File S1, panel A)  10. The N-terminus identified by the Phatr3 gene model in each alignment broadly matches the N-termini identified for orthologous sequences from other ochrophytes (File S1, panel B). Furthermore, RT-PCR analysis of six genes within this dataset amplified genes with product length predicted by Phatr3 (File S1, panel C). 4) Merged gene models, genes that are formed by merging two or more Phatr2 genes into one Phatr3 gene model.
This is substantially greater than the ~ 60% of P. tricornutum genes previously identified to have orthologues in other groups  7  , underlining the importance of dataset size and taxonomic sampling when considering gene conservation  11. Up to 251 different conservation patterns were identified across the entire genome, thirteen of which each accounted for 100 genes or more (Fig. 1 ). Many of the genes were found to have broad distributions across the tree of life, with 4,543 genes (37. 3%) found in at least five of the nine groups considered. These included 203 genes (1. 7%) in all groups studied, and 1074 (8. 8%) in all groups except viruses (Fig. 1 ; Table  S2 ). A further 1,188 genes (9. 8%) were universally found across all eukaryotic groups but neither in prokaryotes nor viruses, hence might constitute eukaryote-specific genes (Fig.
1 ; Table  S2 ). We still found, with the expanded dataset, that many genes within the P. tricornutum genome have limited evolutionary conservation, with 5750 genes (47. 2%) having originated within the recent vertical history of the stramenopile lineage. A total of 3,170 genes (26. 0%) were found to be specific to P. tricornutum, 1,929 were only shared between P. tricornutum and other diatoms (15. 8%), and 651 were only shared with diatoms and other stramenopiles (5. 3%) (Fig. 1 ). We found only limited evidence for genes that were not shared between Phaeodactylum and other diatoms, but were shared with other groups (410 genes, 3. 4%), or for genes that were not shared between Phaeodactylum and other stramenopiles but were shared with other groups (242 genes, 2. 0%), suggesting largely vertical recent inheritance of the Phaeodactylum genome (Table  S2 ).
Prokaryotic genes Across the entire dataset, 584 genes yielded top BLAST hits against prokaryotes (Table  S3 ), which is similar to the number of prokaryotic genes (587) identified in the initial publication of the P. tricornutum genome  7. Similarly to the initial genome publication, the prokaryotic sub-category that produced the most top hits (235) was the proteobacteria (Table  S3 ; Fig. S2A ). Nine other sub-categories (cyanobacteria, firmicutes, chlorobi, archaea, actinobacteria, chlamydiae, chloroflexi, the Deinococcus-Thermus clade, and planctomycetes) contributed more than ten hits each (Fig. S2A ; Table  S3 ). The 15 gene transfers involving members of the Deinococcus-Thermus clade are of particular interest, as this lineage has not previously been reported to have specifically exchanged genes with an ancestor of P.
tricornutum  7. We considered whether the prokaryotic genes present in the P. tricornutum genome are recent acquisitions (e. g. , species-specific), or occurred at earlier points in the evolution of diatom lineages. This was not possible in the initial genome, for which the only other available diatom genome was for the centric species T. pseudonana  7, 8. For this, we performed an analysis in which we serially removed the closest relatives of P. tricornutum in our sequence library (which includes seven complete diatom genomes and transcriptomes for a further 92 diatom species available through MMETSP)  11, 36  , and assessed the number of prokaryotic genes that could be identified in each analysis. Twenty two of the prokaryotic genes were identifiable with the full dataset (hence were specifically acquired by P.
tricornutum following its divergence from other diatoms), 69 were identifiable with a full dataset excluding pennate diatoms (hence were presumably acquired within the evolutionary history of the pennate lineage) and 202 were identifiable with the full dataset excluding all diatoms (hence were acquired during the early evolution of diatom lineages, prior to the division of pennate lineages from their closest relatives within the polar centric diatoms), with the remaining 291 showing more ancient origins (Table  S3 ). Thus multiple gene transfer events involving prokaryotes have occurred progressively through the evolution of ancestors of P. tricornutum.
Red algal genes Across the entire dataset, 459 genes produced BLAST top hits against members of the red algae, consistent with the red algal ancestry of the diatom plastid  4, 20  (Fig. 2A ). This is broadly equivalent to the number of red genes identified in previous studies of diatom plastids  21. The two sub-categories with the greatest contributions to these genes were the Porphyridiophytes (150 genes) and Bangiophytes/ Florideophytes (147 genes) (Fig. S2B ; Table  S3 ). A total of 353 of the red algal genes were identified following removal of all ochrophyte sequences from the dataset, with only a further 28 identified following the removal of aplastidic stramenopile groups (oomycetes, labyrinthulomycetes, and slopalinids) and a further 25 identified following the removal of the two remaining SAR clade groups (ciliates, and aplastidic rhizaria) considered (Fig.
Up to 685 of the identified green genes had orthologues (as confirmed by the reciprocal best-hit (RbH) analysis) in two or more red sub-categories, 314 had identified orthologues in two or more subcategories each of red algae, green groups, amorphea (opisthokonts, amoebozoa and excavates)  19  and prokaryotes, and 222 had identified orthologues in all five of the red sub-categories and all eleven of the green sub-categories considered (Fig. S3A ). We saw no difference in the representation of red and green algal sub-categories in genes with annotated red or green origin (Fig. S3B ). Secondly, green gene transfers appear to have occurred at a different time point to the red algal gene transfers. Although the largest number of putative green genes (805) were identified with the dataset from which all ochrophyte groups were removed (Fig.
2B ), nearly as many (691) were identified following the removal of aplastidic stramenopiles from the dataset (Fig. 2A ). This contrasts to the situation for red genes (which were overwhelmingly identified following the removal of all ochrophyte sequences from the library, as discussed above; Fig. 2A ), and might potentially indicate two distinct gene transfer events between the green algal and stramenopile lineages: an early transfer of green genes in an ancestral stramenopile ancestor, and a subsequent transfer of green genes into an ochrophyte plastid, possibly concomitant with or mediated via the acquisition of a chimeric ochrophyte plastid  10. In summary, our data therefore supports previous findings  10, 21  of gene transfers between an ancestor of stramenopiles and one or more groups of chlorophyte algae.
Like most unicellular eukaryotes and unlike metazoans, P. tricornutum shows a higher rate of IR than ES, supporting the hypothesis that ES has become more prevalent over the course of metazoan evolution  32. We then considered the expression dynamics of P. tricornutum genes that undergo IR or ES (Condition used: WT, Bio sample accession: SAMN06350643). Surprisingly, we found that genes that can undergo intron-retention are more highly expressed than genes that do not show alternative splicing (two sample t-test, P-value < 0. 008, Fig  4B ). This is in contrast to the situation in mammals in which intron-retention down-regulates the genes that are physiologically less relevant  40. To further assess the biological role of alternative splicing, we identified 1341 genes showing IR and 1099 genes with ES during an 18-hour time course under nitrogen-free growth conditions (see Methods).
Following the expression dynamics identified above, the GO categories over-represented in IR datasets show increased expression levels, and the GO categories in ES datasets diminished expression levels, from the point of induction over the length of the time-course. Many of the GO categories that show differential IR or ES dynamics have plausible functions in tolerating nitrogen starvation. For example, many of the genes that show enhanced intron-retention following 45 minutes of starvation are implicated in cell cycle regulation (e. g. , functions in chromosome separation, histone modifications, or the mitotic cell-cycle checkpoint), which might correspond to an arrest of the cell cycle under nitrate limitation  41. Similarly, genes implicated in catabolism and storage of cellular nitrogen pools (allantoin biosynthesis, glutamine biosynthesis, and glutamate catabolism) and nitrate and nitrite transport show enhanced intron-retention within 45 to 90 minutes of starvation induction  42.
Data generation and mining Phaeodactylum tricornutum genome re-annotation (named as Phatr3) was done on the Phatr2 genome assembly (ASM15095v2). The Phatr2 assembly was generated by the Joint Genome Institute (JGI), which resulted in 10,402 gene models from 33 assembled scaffolds (12 complete and 21 partial chromosomes) and 55 unassembled scaffolds  7. Gene models were predicted from RNA-Seq mapping and aligning the EST data-set using est2 genome. Additionally we used SNAP and Augustus and MAKER2 for final gene predictions. Apart from the previous assembly information, the species-specific data used in this re-annotation included the following.
RNA-Seq Multiple RNAseq libraries (103 libraries in total) were generated under different growth conditions and are being used for many functional studies  47. The growth conditions used can be broadly divided into two major categories: 1) Nitrogen availability, which include 30 RNAseq libraries generated using Illumina platform (Bio-project accession no. PRJNA311568; Bio-sample accession numbers SAMN04488978-SAMN04489007), and 2) Iron availability, includes 49 libraries of RNA-Seq generated using SoLiD sequencing technology (SRA: SRP069841)  47. Apart from these 91 libraries, 12 more RNAseq libraries were generated including the wild type and alternative oxidase (Phatr2_bd1075) mutants  48  ; Bio-sample accessions: SAMN06350641-SAMN06350652. More information about the culture conditions can be referred from File S2.
Construction of a multi-sequence reference dataset A composite reference library, consisting of 75001602 non-redundant protein sequences was compiled from UniPROT (http://www. uniprot. org/help/uniref) (downloaded February 2016), alongside additional genomic and transcriptomic resources from JGI, MMETSP, and the 1kp project currently not located on UniPROT (http://genome. jgi. doe. gov; http://marinemicroeukaryotes. org/; https://sites. google. com/a/ualberta. ca/onekp/) (Table  S4 )  17. To minimize artifacts arising from contamination between different MMETSP libraries, each MMETSP library was first pre-cleaned using a BLAST pipeline as described previously  52  which identifies a custom similarity threshold between each constituent library above which sequence pairs are inferred to be contaminants.
Structural re-annotation The P. tricornutum version 2 genome (JGI), published in the year 2008  7  , was comprehensively structurally re-annotated using multiple RNA sequencing libraries, non-redundant ESTs and updated repertoires of stramenopile proteomes. 42 RNA-Seq libraries generated using an Illumina sequencing platform were mapped to the genome using Genomic Short-read Nucleotide Alignment Program, GSNAP  53  , integrated within mapping pipeline of Ensembl Genomes  54. The remaining 49 SoLiD sequence libraries were aligned to the genome in color space  55. The alignment file for each mapped library can be accessed from ftp://ftp. ensemblgenomes. org/pub/misc_data/bam/ protists/phatr3/. The mapping percentage was estimated using Samtools and varied between 70 -96% (Table  S9 ). Transcript assembly was further executed using Cufflinks 56 with default parameters.
Functional re-annotation All predicted gene models were annotated for protein function using InterProScan 61 as part of the Ensembl protein features pipeline  62   Next, the presence of organelle signaling signatures within the entire Phatr3 gene repertoire was further investigated using ASAFind and HECTAR, under the default conditions as specified in the original publications for each program  63, 64. HECTAR was run remotely, using the Galaxy integrated server provided by the Roscoff Culture Collection (http://webtools. sb-roscoff. fr/).
Distribution of epigenetic marks Data corresponding to histone H3 post-translation modification marks, H3K27me3; H3K9me2/3; H3K14_Ac; H3K4me2 and DNA methylation (CG, CHH, CHG), were taken from  15  and  16, 51  , respectively. RNA-Seq data for Pt18. 6 (normal growth condition) was also downloaded from the same resource. Distribution of all marks along with the expression were then checked over the new genes and new transposable element (TE) models by adapting the methodology applied in  15  and  16. Estimation of expression (normalized DESeq counts) and differential gene expression analysis at different stages of the study was performed using Eoulsan  65  , using parameters as indicated in Eoulsan parameter file used, (File S3). Statistical analysis to compare the expression of genes was performed using two-sample t-test with unequal variance.
Alternative splicing To explore the set of genes undergoing alternative splicing, exon skipping or intron retention, 17 RNA-Seq samples prepared under different conditions of nutrient availability (Biosample accessions: SAMN06350643, SAMN06350647, SAMN04488984, SAMN04488988, SAMN04488978, SAMN04488992, SAMN04488980, SAMN04488981, SAMN04488985, SAMN04488979, SAMN04488989, SAMN04488983, SAMN04488987, SAMN04488991, SAMN04488982, SAMN04488986, SAMN04488990) were compared. Only genes that were annotated as having two or more exons, and containing introns with a minimum length of 50 bp, were considered for the analysis. RNA-Seq reads were mapped on the reference genome using Bowtie  68  with parameters: -n 2 -k 2 --best. To filter the significant candidate features, we considered horizontal (along the gene) and vertical coverage (depth of reads) to be more than 80% and 4x, respectively.
The occurrence of an individual biological process within a specific functional set (genes exhibiting intron-retention/exon-skipping, etc. ) was compared to that of its occurrence in the complete annotated Phatr3 biological process catalog. The degree of significance of enrichment of each biological process was quantified using a chisquared test, with a threshold significance P value of 0. 05. To gain insights into the role of alternate splicing in regulating the molecular physiology of the cells, the expression patterns of AS candidates predicted to undergo IR and/or ES under nitrogen replete conditions (Biosample accessions: SAMN04488981, SAMN04488985, SAMN04488979, SAMN04488989) were compared at different time-points (T15min, T45min, T90min and T18hrs (referred as Tend) to the wild-type (Biosample accession: SAMN06350643).
Annotation of repetitive elements The REPET v2. 2 package  69  was used to detect the repetitive fraction of the P. tricornutum genome. The TEdenovo pipeline (https://urgi. versailles. inra. fr/Tools /REPET) was launched including the Repeat Scout approach  70  to build a library of consensus sequences representatives of the repeated elements in the genome assembly. The classification comes from decision rules applied to the evidence collected from the consensus sequences. These include: search for structural features, search for tandem repeats, comparison to PFAM, comparison to Repbase  71  and to a local library of known TEs. The library of manually curated TEs that has been established in previous work  44  as appended to the TE denovo library and redundancy was removed from the combined library.
The TE annotation pipeline was then run with default settings using the sequences from the filtered combined library as probes. a suspected history of secondary endosymbiosis other than ochrophytes were removed. Note that in this case a separate value for a reference library excluding SAR and CCTH clade taxa is not provided, as all it is not known when secondary endosymbioses arose within CCTH clade lineages  19  , hence all CCTH clade taxa were excluded from the analysis. (B) Comparison of the proportion of genes that yield top hits against five different categories (all prokaryotes, the Deinococcus-Thermus clade only, all red algae, and green groups, and all amorphea) following the removal of different groups from the complex algae-free dataset. Total number of genes within each category is indicated beneath it in round brackets.
Data labels for each value are provided in the corresponding segment; where the segment is too small to accommodate the corresponding value, the value is placed outside the chart, and shaded to match the color of the segment. The largest value recorded for each gene category corresponds to the most probable evolutionary time point at which genes were acquired; for example, the largest number of genes of red algal affinity were recovered following the removal of all algae with plastids of secondary or higher endosymbiotic derivation from the reference dataset, indicating a large-scale donation of red algal genes into algae with plastids of secondary or higher endosymbiotic derivation. The overwhelming majority of the BLAST top hit analyses support monophyly of the ochrophytes, and at least three quarters retrieve the same ochrophyte sister-group as determined through single-gene tree analysis.
Acknowledgements and Funding We thank  Stephane Le Crom  and  Laurent Jourdren  for helping setting up EOULSAN. CB acknowledges funding from the  ERC   Advanced Award ''Diatomite'' , the  Louis D Foundation of the Institut de France , the  Gordon and Betty Moore Foundation , and the  French Government ''Investissements d'Avenir'' programmes MEMO LIFE  ( ANR-10-LABX-54 ),  PSL* Research University  ( ANR-1253 11-IDEX-0001-02 ), and  OCEANOMICS  ( ANR-11-BTBR-0008 ). CB also thanks the  Radcliffe Institute of Advanced Study at Harvard University  for a scholars fellowship during the 2016-2017 academic year. AR was supported by a MEMO LIFE PhD scholarship and RGD was supported by an  EMBO   Long-Term Fellowship  ( ALTF 1124-2014 ).
Therefore this lack of reliable data is damaging for the development of models for turbulence and combustion  [4, 5, 6]. The objectives of the research program REFINE (Real-gas Effects on Fluid Injection: a Numerical and Experimental study) are to bridge the gap between experimental and numerical observations in providing reliable experimental data and new strategies of modeling. The experimental aspect is presently addressed through the development of a new test-bench (next section) and for which a first qualitative analysis is presented. Indeed, many combustion devices involve thermodynamic phase transition from subcritical to supercritical state as in high-pressure combustion chambers of rocket or diesel engines. When the pressure exceeds the critical pressure pc of any substance, the distinction between gas and liquid vanishes and the injection and combustion are quite different from classical known behaviors observed at low pressure.
DOWS This section describes HyBIS, our solution combining FMA techniques with the VMI approach. As mentioned above, the main goal of our work is to improve Windows security in virtualized environments. In particular, our research focuses on protecting such OS from rootkits. In order to protect Windows from modern rootkits, we mostly focused our studies on the RAM component. Memory, in fact, stores both code and data and is involved in almost every operation performed on the machine during the OS execution. Thus, RAM can be considered the most complete source of information about the status of a running OS at a specific time. For such a reason, Modern rootkits use to manipulate memory to conceal their activities and resources. Nonetheless they reside in RAM while running, thus giving the opportunity to detect their presence.
Integrating Rekall Since Rekall is written in Python, an interpreter must be present on the host system. For incorporating Rekall, it has been necessary to import the Python C++ library into the VirtualBox source code. The Rekall functionalities, instead, can be used by means of the provided API library, as mentioned in the previous section. In order to make use of the forensic analysis functions, a suitable session has to be set up. A Rekall session represents a specific combination of a dump file and a selected profile. When starting a session, if a valid profile for the current kernel version is found, every compatible Rekall plugin automatically becomes available to be used for a forensic analysis.
RELATED WORK In this section we survey most relevant related work and stress main differences with respect to our solution. Zhang et al. [37]  leverage SMM, an advanced x86 execution mode, for detecting memorybased stealthy malware. Their SPECTRE framework can introspect a live operating system and supports both Windows and Linux OSes. However, this framework is vulnerable to hardwarebased attacks, such as  [34]. Furthermore, their work is limited to Windows XP SP3. In  [38] , Hizver and Chiueh make use of Volatility for the analysis of VM execution states. Their RTKDMS system is able to perform real-time monitoring at the hypervisor level. Differently from HyBIS, such an architecture leverages an additional VM for the introspection analysis. Again, the experiments are limited to Windows XP.
Furthermore, their system does not tackle the rootkit threat specifically, neither it explores any reaction possibility. Deng et al. [18]  propose the SPIDER archicture, a stealthy program instrumentation and debugging framework built upon hardware virtualization. SPIDER enables monitoring memory read/write at any address. Nonetheless, unlike HyBIS, it requires an in-guest agent which modifies the guest OS kernel. In  [19] , Lengyel et al. describe DRAKVUF, a novel dynamic malware analysis system based on Xen, which improves hardware resources usage efficiency. DRAKVUF takes advantage of the hardware virtualization extensions to provide a transparent and scalable environment to enable in-depth analysis of malware samples. In this case, the target OS is Windows 7 SP1 in both 32-and 64-bit versions.