dataesr/scientific-paragraphs-categorization huggingface dataset:
The distance between two data points corresponds to half-beam spacing to ensure the data points are independent of each other, and the specific angular momenta plotted are averaged over a region of 3 × 3 pixels. Only the sources for which we had at least four data points (i. e. more than two beams across) for either the redshifted or blueshifted sides are shown. Therefore, this criterion excludes the core IRAS 23385. We can compare the angular momentum of each source with some estimate for the maximum value above which the core becomes gravitationally unbound. Because neither the rotation nor the density profile is expected to be uniform, we follow the approach of  Zhang et al. (2018) , assuming the structure is flattened with its mass growing linearly with radius (see  Mestel disk: Mestel 1963).
5. 2 and Table  7 ). Protostellar masses were estimated from fitting the PV diagrams of CH 3 CN transitions and other dense gas tracers as discussed in Sect. 5. 3. We select the upper limit of the dynamical mass estimates for the protostellar masses to calculate upper limits for the Toomre Q parameter. When the dynamical mass upper limits are higher than the masses derived from the source luminosities, we select the luminosity derived masses. For sources that were studied in more depth using the same CORE data (IRAS 23033:  Bosco et al. 2019; IRAS 23385: Cesaroni et al. 2019) , we adopt the mass estimates reported by these authors. All mass estimates and the chosen protostellar masses are summarised in Table  7. Assuming the protostar is positioned at the location of the continuum peak, we calculate the Toomre Q parameter for each disk candidate, accounting for the self-gravity of the disk by including the enclosed gas mass at each radius.
Toomre unstable disks There exist regions with Q ≲ 2 for most disk candidates (see Fig. 7 ), meaning they are unstable against gravitational instabilities and prone to fragmentation. While some candidate disks appear to be Toomre unstable almost entirely (e. g. G75. 78, W3(H 2 O) E and W, IRAS 23385, NGC 7838 IRS1), others show signs of instability only in their outskirts (e. g. NGC 7538 S). For some sources, the low Q values are coincident with the elongation of the continuum, hinting at the existence of underlying substructure. The best examples for such cases are IRAS 23385, and NGC 7538 S. In fact, the in-depth analysis of  Cesaroni et al. (2019)  using the CORE survey data confirms that IRAS 23385 is forming a cluster of massive stars (marked by green plus symbols in Fig. 7 ). Another interesting case worth noting is IRAS 21078, for which the low Q values are coincident with a secondary source detected in continuum, ∼0.
Acknowledgements. The authors would like to thank the referee,  Adam Ginsburg , whose comments helped improve the clarity and impact of the paper. A. A. and H. B. acknowledge support from the  European Research Council  under the European Community's  Horizon 2020 framework programme  ( 2014-2020 ) via the  ERC Consolidator Grant  ' From Cloud to Star Formation (CSF) ' (project number  648505 ). RK acknowledges financial support via the  Emmy Noether  and  Heisenberg Research  Grants funded by the  German Research Foundation (DFG)  under grant no. KU 2849/3  and  2849/9. RGM acknowledges support from  UNAM-PAPIIT  project  IN108822. AP acknowledges financial support from the  UNAM-PAPIIT   IN111421  grant, the  Sistema Nacional de Investigadores of CONACyT. RGM and AP also acknowledge support from the  CONACyT  project number  86372  of the ' Ciencia de Frontera 2019' programme , entitled ' Citlalcóatl: A multiscale study at the new frontier of the formation and early evolution of stars and planetary systems ',  México.
This paper investigates channel coding for DNA data storage, a recent and emerging paradigm in which both substitution errors and synchronization errors (insertions, deletions) are introduced in the stored data. The paper first proposes a novel and accurate statistical channel model to represent errors introduced by the DNA data storage support. The channel model takes the form of a k-order Markov Model which allows to capture both memory in the sequence of channel errors, and statistical dependency between errors and input sequence. The model probabilities are inferred on two sets of data: one set of experimental data, and one set of genomic data. In both cases, we observe a superior accuracy, evaluated from the Kulback-Leibler divergence, of our channel model compared to existing ones. Second, the paper investigates the design of error-correction solutions dedicated to the proposed channel model.
I. INTRODUCTION DNA data storage is seen as an emerging and promising alternative to conventional storage supports, due to higher density and greater durability. In DNA data storage, information is stored by synthesizing a digital sequence into a DNA molecule built from a sequence of bases A,C,G,T. The original information can be retrieved by using a DNA sequencer such as the Oxford Nanopore Technology (ONT) MinION sequencer which we consider in this work. Recent progress in both the synthesis and sequencing technologies pave the way for the development of cost-effective and competitive DNA data storage solutions in the next few years. Unfortunately, sequencing still introduces a large amount of substitution, insertion, and deletion errors in the read data. The associate editor coordinating the review of this manuscript and approving it for publication was Parul Garg.
Insertions and deletions break sequence synchronization, and they cannot be corrected with conventional error-correction methods designed for data storage or wireless communications. Therefore, from a channel coding perspective, two key challenges should be addressed in order to make DNA data storage more practical: (i) develop accurate statistical channel models to represent errors that occur during the DNA data storage process, (ii) design powerful error-correction solutions that can handle the three types of errors. This paper aims to address both of these challenges in a cohesive manner, by first introducing an accurate channel model and then incorporating the model knowledge into the decoder. Statistical channel models allow to run extensive insilico simulations, before implementing expensive in-vitro experiments.
This paper introduces a new and accurate statistical channel model that is defined by a set of probabilities over the error events and does not require any additional parameter. Given that the MinION sequencer reads k symbols at a time, our channel model relies on a k-order Markov Model in order to take into account both memory in the successive error events, and statistical dependency between errors and input sequence. We describe how to train the model using two different datasets: one smaller dataset of experimental data passed through the entire DNA storage process, and one larger dataset of genomic data. Numerical evaluation of the Kullback-Leibler divergence demonstrates that our model more accurately represents the actual DNA data storage process than existing models  [6] ,  [7] ,  [10].
Lastly, we introduce a dynamic channel model, which allows to smoothly adjust the channel probabilities inferred from the datasets, in order to simulate a wider range of error probabilities in numerical experiments. It is worth mentioning that in our previous work  [11]  where the channel model was introduced, only one dataset was used for training, and the dynamic channel model was not presented. Second, this paper addresses the design of efficient error-correction solutions for DNA data storage. The first error-correction solutions proposed for synchronization errors were based on Varshamov-Tenengolts (VT) codes  [12] , which can correct only one deletion or one insertion. These solutions were further extended to correct one burst of insertions and deletions  [13] ,  [14] , or a few errors  [15] ,  [16] , or to consider constrained codes together with the correction of a single error  [17].
However,  [26]  only introduced the new space of states, while we describe the practical implementation of all the decoding steps. In addition,  [26]  evaluated the theoretical performance of the decoder in terms of achievable information rate, while we present numerical BER and FER values to evaluate the practical decoder performance. The outline of the paper is as follows. Section II describes the DNA data storage workflow. Section III-B introduces the channel model with memory. Section IV presents the considered concatenated code construction. Section V explains how to integrate our channel model into convolutional decoders. Section VI shows numerical results.
Then, a software called basecaller transforms the sequences of current levels into digital sequences. In this work, we employed Guppy and Bonito basecallers, which are both built from Deep-Learning approaches. We use y (j)  to denote the J digital sequences output by the basecaller, where j ∈ 1, J , and y (j) t takes values in a quaternary alphabet {A, C, G, T } or GF  (4). The read sequence y (j) is of length P (j) , and typically P (j) ̸ = N. This is because the sequencer and the basecaller not only introduce substitutions, but also insertions and deletions in the read sequences, and error patterns vary from sequence to sequence  [5].
1) SET of EXPERIMENTAL DATA (SetE) We first used a set of experimental data, callet SetE, that passed through the full DNA data storage process described in Section II. This set was generated from U = 9 input sequences x (u) (u ∈ 1, U ), called the reference sequences, each providing J u output sequences y (u,j) (u ∈ 1, U , j ∈ 1, J u ), for a total of J = 9 u=1 J u = 34604 read sequences obtained after basecalling. In addition, 6 of the reference sequences have N = 500 nucleotides, and the 3 other ones have length N = 1000 nucleotides. As a first step for training, each read sequence y (u,j) was aligned with its reference sequence x (u). The alignments were done using the ggsearch36 tool from the FASTA software  [30]. This tool performs global to global alignments, that is to say that the whole read (from first to last base) is aligned against the whole reference.
In a second step, we used the aligned reads to estimate conditional probabilities involving each k-mer contained in the sequences x (u). For instance, we estimated the conditional probabilities P(e t = D|kmer t = β, e t-1 = D ′ ) as N(e t = D, kmer t = β, e t-1 = D ′ ) N(kmer t = β, e t-1 = D ′ ) (9) where N(. ) counts the number of occurences of the event over all the aligned-read pairs (x (u) , y (u,j) ), D, D ′ ∈ {Ins, Del, Sub, Match}, and β is a specific k-mer that can be any sequence of k bases. The other probability terms were estimated by following the same approach. At the end, the overall error probability over this dataset is high, about 10%. The first advantage of this set of experimental data is that the reference sequences x (u) are perfectly known, which makes the comparisons between the reads and the reference very reliable.
On the other hand, this set contains only a small amount of data, due to high DNA synthesis costs. This leads to plenty of unobserved combinations (kmer t , e t-1 = E), over the 4 k+1 possible ones. In our case, when a given combination (kmer t , e t-1 = E) was left unobserved, we estimated the corresponding probabilities by averaging over all observed combinations. 2) SET of GENOMIC DATA (SetG) Genomic data are much more accessible than experimental data, as they can be obtained from various genomic databases. In this work, we considered a set of genomic data called SetG, that contains U = 7 strains (sub-types)  [31]  of the Streptococcus thermophilus bacteria  [32]. In this case, the reference sequences x (u) contain about 10 6 bases, while the reads y (u,j)  correspond to subsequences of x (u) , and contain between 10 3 and 10 5 bases.
This makes it impossible to use global alignments. In addition, there is no ground truth that states from which part of the reference x (u) a given read y (u,j) is produced. Thus, for the genomic dataset, we performed local alignments with a tool called minimap2  [33] , which is very efficient in performing local alignments of very long sequences in a timely manner. In addition, we eliminated from the training set the reads y (u,j) with a low-scoring alignment. We then used the same approach described in (9) in order to estimate all the probability terms. The overall error probability over this dataset is about 3%, which is significantly lower than for the experimental dataset. Note that since for SetE the reference sequences x (u) and the reads y (u,j)  have the same length, global alignment is equivalent to local alignment.
The main advantage of genomic data is that it offers a significantly larger quantity of data. All possible combinations (e t = D, kmer t = β, e t-1 = D ′ ) were observed, with a sufficient amount of each. However, this dataset introduces another type of bias since the training discards low-scoring alignments, i. e. , sequences that have a high amount of errors. This explains why it gives a lower error probability compared to the experimental dataset.
D. DYNAMIC CHANNEL MODEL The two datasets provide two realistic channel models, with similar underlying characteristics but varying error rates. This will allow for the evaluation of the proposed error-correction codes under different conditions. To expand the performance evaluation even further, we propose a modified version of our channel model, which enables to consider various and intermediate levels of errors. This modified channel is referred to as the dynamic channel model. Starting from a set of conditional probabilities obtained from one or the other training dataset, we introduce a unique scaling parameter α, and evaluate a new set of probabilities P (α) (e t = D|kmer t = β, e t-1 = D ′ ) defined for D ∈ {Ins, Del, Sub} as P (α) (e t = D|kmer t = β, e t-1 = D ′ ) = αP(e t = D|kmer t = β, e t-1 = D ′ ), (10) and for D = Match as P (α) (e t = D|kmer t = β, e t-1 = D ′ ) = (1 -α)P(e t = D|kmer t = β, e t-1 = D ′ ).
1) CHANNEL STATISTICS We now show some statistics of our model trained from the set of genomic data SetG. Indeed, in SetG, all k-mers appear a sufficient amount of time, which avoids any bias that could be introduced by unobserved k-mers. In this part, we consider k = 6, which is in accordance with the way the MinION sequence works. First, Figure  7  shows the repartition of probabilities over k-mers for each event INS, DEL, SUB, depending on the TABLE 1. For the model trained on SetG, over 4096 k-mers with k = 6, number of times the largest probability is for event either INS, DEL, or SUB (columns) with respect to the previous error event (rows). For instance, when the previous error event is a Match (fist row), for 687 of the k-mers, the probability of an insertion is larger than the probability of a deletion or a substitution.
2) KL DIVERGENCE We now compare our model against three different existing models: (i) the i. i. d. model, (ii) DeepSimulator, as a representative of DL-based methods, (iii) BadRead, for which event probabilities directly depend on the k-mers, as in our model. We here consider the two datasets, namely SetE and SetG, given that they have some different characteristics. Starting with the set of experimental data SetE, Figure  8  shows the KL divergence with respect to the memory order k for the i. i. d. channel, DeepSimulator, BadRead, and our model. The channel models were all trained on SetE, except for DeepSimulator. Then, the KL divergence was averaged over the sequences of SetE. First note that only the curve for our model varies with k, because this is a parameter of our model only. Second, we observe that our model has the lowest KL divergence, while the i.
i. d. model surprinsingly comes second. We see that for our model, k = 1 gives a better KL divergence than the i. i. d. model, because it still takes into account memory in successive events e t , and dependency with the input symbol x t. In addition, we see that for our model, when k is large enough, the KL divergence is close (but not equal) to 0, which means that our model trained on SetE represents accurately the dataset SetE, even for a value of k as small as 9 or 10. Then, in Figure  9 , BadRead and DeepSimulator were taken from their own knowledge (from the online code), while our model as well as the i. i. d. model were trained on SetG. The KL divergence values were then calculated over SetE. One first important remark is that in this case, the KL divergence values are much higher than in Figure  8 , because the models were trained on a dataset and evaluated on another one.
B. CC DECODERS PERFORMANCE We now evaluate the performance of the three CC decoders, by running Monte-Carlo simulations over our memory channel model. We consider the two versions of our memory channel model, i. e. , the one trained on the experimental data SetE, and the one trained on the genomic data SetG. Both versions are considered because the overall error rates differ between the two models, as explained in Section III. Each simulation run generates a random binary sequence w, uniformly at random. Then w is encoded with a (k c = 1, n c = 2, K c = 3) CC of rate 1/2, which uses the generator polynomial δ poly = [δ 2 + 1, δ 2 + δ + 1], and outputs a binary encoded sequence x of a certain length N. We consider values N ∈ {54, 204}, which falls in the range of length of DNA  molecules produced by current synthesis techniques  [39].
VII. CONCLUSION This paper introduced a new channel model for DNA data storage, which can be seen as a Markov model of order k and was trained on experimental and genomic datasets. The proposed model was shown to be more accurate than existing ones through numerical evaluation of the KL divergence. Additionally, this work improved the CC decoder of  [19]  by incorporating the knowledge of the channel model, resulting in better BER and FER performance. Future works will focus on reducing the complexity of the proposed decoder.
Recent advances in DNA sequencing technologies particularly long-read sequencing, greatly improved genomes assembly. However, this has created discrepancies between published annotations and epigenome tracks, which have not been updated to keep pace with the new assemblies. Here, we used the latest improved telomere-to-telomere assembly of the model pennate diatom Phaeodactylum tricornutum to lift over the gene models from Phatr3, a previously annotated reference genome. We used the lifted genes annotation and newly published transposable elements to map the epigenome landscape, namely DNA methylation and post-translational modifications of histones. This provides the community with PhaeoEpiView, a browser that allows the visualization of epigenome data and transcripts on an updated and contiguous reference genome, to better understand the biological significance of the mapped data.
Besides gaining fundamental insights into transcriptional regulation and metabolic pathways of microalgae genomes, epigenetics offers the potential to facilitate microalgae breeding programs for the production of biomass and high value substances 13. While rapid growth rate and metabolic plasticity (photoautotrophy/mixotrophy), ease of transformation and cryogenic preservation are distinguishing features for a model microalga, P. tricornutum has become a prominent model organism, due to its sequenced genome and high-quality annotation. This has provided a valuable resource for expanding its molecular toolbox, including expression databases, genetic editing tools and epigenome landscape. The genome of the model diatom Phaeodactylum tricornutum CCAP 1055/1 and the corresponding Phatr2 annotation were published in 2008 using whole genome shotgun paired-end Sanger sequencing (NCBI assembly ASM15095v2)  14.
datasets, along with published expressed sequence tags and protein sequences  15. The first assembly of the genome contained 33 scaffolds among which 12 telomere-to-telomere chromosomes. Using long-read sequencing, Filloramo et al. 16  re-examined P. tricornutum assembly which led to additional sequence information, but did not improve the continuity and chromosome-level scaffolds compared to the original reference genome  16. An approach combining long reads from the Oxford Nanopore minION platform and short highly accurate reads from the Illumina NextSeq platform, was recently used to reassemble the genome of P. tricornutum. This resulted in a significant improvement with the identification of 25 nuclear chromosomes  17. However, despite this update, the Phatr3 annotation of P. tricornutum has not been revised to reflect the new assembly, which is a common occurrence for many species where annotations lag behind the latest improvements in genome assemblies.
Minimap is used with 50 secondary mappings, end bonus of 5 and chaining score of 0. 5. Genes are lifted and considered mapped successfully if the alignment coverage and sequence identity in the child features (usually exons/CDS) is ≥ 50%. We used two different strategies of annotation of gene structure on the new assembly: 1. Whole genome-based reference alignment of all genes. 2. Chromosome-by-chromosome liftover. RNA sequencing from the reference ecotype (Pt1 8. 6) was then aligned to both the current and previous genome versions with BWA-mem2 2. 2. 1  24  and transcripts quantification was performed with HTSeq 0. 13. 5  25  using the gene annotation corresponding to each genome. Transcript quantification and gene lengths were then compared between the two versions using R (URL https:// www.
R-proje ct. org/). Chromatin extraction and immunoprecipitation. Chromatin isolation was performed as described previously  26  with few modifications. Briefly, the incubation step in buffer II is repeated several times until the pellet becomes white. Each ChIP-Seq experiment was conducted in two independent biological replicates. Monoclonal antibodies from Cell Signaling Technology were used for immunoprecipitation, H3K9me3 (13969), and H3K27me3 (9733). Satisfactory monoclonal antibodies were not accessible for the other histone marks examined in previous studies. ChIP-Seq analysis. Pair-end sequencing of H3K9me3, H3K27me3 ChIP and input samples was performed on NovaSeq 6000 with read length of 2 × 150 bp. Previously published ChIP sequencing for H3K9me2, H3K9me3, H3K4me2, H3K27me3, H3K9/K14Ac and H3K4me3 were retrieved from NCBI's Gene Expression Omnibus accessions GSE68513 and GSE139676  9, 10.
Raw reads were filtered and low-quality read pairs were discarded using Trim Galore 0. 6. 7 (https:// doi. org/ 10. 5281/ zenodo. 51278 99) with a read quality (Phred score) cutoff of 20 and a stringency value of 3 bp. Using the 25 to 25 telomere assembly published in 2021 as a reference genome, the filtered reads were mapped using Bowtie2 2. 4. 5  27. We then performed the processing and filtering of the alignments using Samtools 1. 15 "fixmate -m" and "markdup-r" modules  28. Two biological replicates for each ChIP were performed and read counts showed a good Pearson correlation by Deeptools multiBamSummary v3. 5. 1 with a bin size of 1000 bp  29. To identify regions that were significantly enriched, we used MACS2 v2. 2. 7. 1  30  on the combination of the two replicates with "callpeak --qvalue 0.