dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Being able to predict highly dimensional objects such as appearance can revolutionize breeding by merging genome and phenome information in a coherent framework. Here we present a proof of concept that this is possible, even using very simple network architectures. We show that 2D, but also 3D, shapes can be accurately predicted and generated. The problem posed here is similar to the 'text-to-image' challenge, where algorithms are trained to generate images from figure captions. Some works have recently reported highly accurate results (Ramesh et al. ;  Radford et al. 2021)  and we foresee that 'dna-to-image' should follow. There are some differences between text and DNA that require specific developments though. First, text is divided in a finite, relatively small number of items (words) which relationships can be inferred by automatically parsing large available databases.
DNA sequence can be split into coding / noncoding, introns / exons but cannot be assimilated to 'words' with specific meanings. DNA or marker data are not segmented; their relationships are much more intricate than those in words from human languages and are unknown to a large extent. For instance, most discovered causative mutations that affect shape are located outside coding regions  (Wu et al. 2018; Martínez-Martínez et al. 2022). Second, large corpuses of images and figure captions are available for training text-to-image problems; these datasets are not readily available for fruits or other agricultural scenarios. Finally, texts used to generate images are short and simple; algorithms usually fail and generate unpredictable results if input text is slightly changed. In the case of DNA, the number of differences between strain or individual genotypes is very large; we still do not know how dna-to-image algorithms will cope with this issue.
There are several approaches that can be used to achieve this. One option is the 'attention' mechanism, which is used to underline words of particular relevance in text analysis  (Vaswani et al. ). Another possibility is to define a specific input layer for causative mutations and merging with the rest of SNPs in a separate layer. This is straightforward with standard software such as Keras  (Chollet 2015). Further work is warranted to overcome limitations of this work and continue this area of research. First of all, appropriate datasets of large size in 2D and 3D must be generated. In fact, one of the limiting steps for this methodology to be applied is the lack of datasets of enough size containing high density genotypes and good quality images. The simplest scenario should be fruits, as is the TRADITOM initiative in tomato  (Pons et al.
2022; Blanca et al. 2022) , but many other applications can be envisaged: animal conformation (e. g. , dairy bull catalogs, dog breeds), whole plant appearance, leaf and root morphology, color patterns, … Second, more complex network architectures inspired in current text-to-image algorithms must be adapted to the dna-to-image scenario. Finally, generative models, such as conditional generative adversarial networks (CGANs;  Goodfellow et al. 2014; Mirza and Osindero 2014) , conditional on DNA information, could be used to produce images of high quality. On top of that, new tools for dealing with 3D objects are needed. In summary, we have shown that very simple networks can be successfully trained in small datasets to accurately predict fruit images. Although much work remains to be done, this research opens new possibilities in the area of prediction of complex traits.
Funding Work funded by  Ministry of Science and Innovation-State Research Agency (AEI, Spain ,  10. 13039/501100011033 ) grant numbers  PID2019-108829RB-I00  and  CEX2019-000902-S ,  European Commission   H2020  research and innovation program through TRADITOM grant agreement No. 634561 , and  HARNESSTOM  grant agreement No. 101000716 , and  PROMETEO  projects  2017/078  and  2021/072  to promote excellence groups by the  Conselleria d'Educació, Investigació ,  Cultura i Esports  ( Generalitat Valenciana, Spain ) and by the  CERCA Programme / Generalitat de Catalunya (Spain).
This analysis identified one CNS (called CNS1) conserved in all species, except zebrafish, located 33. 4 kb upstream the Hnf1b transcription start site in mouse and a second one (CNS2) located in the 4th intron of Hnf1b conserved in all species examined (Fig. 1a, b ). A survey of the literature revealed that CNS2 is identical to a previously characterized Hnf1b enhancer whose activity is restricted to Hnf1b neural expression in transgenic mice  10. We therefore focused our analysis on the 306-bp genomic region CNS1. Active regulatory sequences show epigenomic properties such as characteristic patterns of histone modification and accessible chromatin. CNS1 was queried against public databases for Deoxyribonuclease I (DNase I) hypersensitive site sequencing (DNase-seq), Assay for Transposase-Accessible Chromatin sequencing (ATACseq) and chromatin-immunoprecipitation sequencing (ChIP-seq) for histones marks or the transcriptional cofactor p300 from human, mouse (ENCODE project  25, 26  ) and Xenopus 27 embryonic and adult tissues or cell lines.
6 kb downstream the end of the hnf1b flanking gene heatr6 (HEAT repeat containing 6 protein). Synteny is conserved in mouse and CNS1 is located 33. 4 kb upstream the Mus musculus Hnf1b TSS, and 33. 4 kb downstream the end of the Heatr6 gene raising the question of potential role of CNS1 in Heatr6 regulation. Heatr6 is known to be significantly expressed in the ectoderm and neurectoderm in Xenopus embryos, but neither in the developing pronephros (Xenbase, https:// www. xenba se. org), nor in the developing mouse kidney (GUDMAP, https:// www. gudmap. org). Heatr6 is therefore unlikely to be under the influence of CNS1. In contrast, our data clearly argue in favor of hnf1b being a target gene regulated by CNS1: 1/CNS1 drives reporter expression in a subset of hnf1b expression territories 2/ CNS1 is responsive to pax8, which is known to control hnf1b expression in the Xenopus pronephros  19  3/CRISPR/ Figure 6.
Methods Identification of conserved non-coding regions (CNSs). We used the dynamic graphical interface ECR Browser (http:// ecrbr owser. dcode. org/), a tool for visualizing and accessing data from comparisons of multiple vertebrate genomes  24. With minimum similarity of 70% and 100-base pair length as set parameters, sequences were filtered by conservation analysis across different vertebrate species (Mus musculus, Homo sapiens, Rattus norvegicus, Monodelphis domestica, Gallus gallus, Xenopus tropicalis, Danio rerio) using a mouse 157 kb genomic sequence encompassing Hnf1 gene as a reference genome. R-Vista 2. 0 (https:// rvista. dcode. org) was used to identify conserved putative TFs binding sites in CNS1  30. P300 and histones ChIP-seq, DNase-seq and ATAC-seq peaks were displayed using the ENCODE databases (https:// www.
encod eproj ect. org)  25, 26  and database from the Veenstra's laboratory (http:// veens tra. scien ce. ru. nl/ track hubx. htm)  27  for Xenopus. General analyses and visualization were done using UCSC genome browser (https:// genome. ucsc. edu/) and Ensembl genome browser (http:// www. ensem bl. org/). Electrophoretic mobility shift assays. Electrophoretic mobility shift assay (EMSA) analysis was performed using the Light Shift® Chemiluminescent EMSA Kit (Thermo Scientific). Briefly, nuclear extracts were prepared from MDCK cells, while DNA-probes (Pax8-BS, Control TPO, Pax8-BS mut) were biotin-labelled at 5ʹ End (Eurofins). Twenty µG nuclear extracts were incubated with the different biotin-labeled probes on ice for 20 min in a 20 μL mixture. For competition EMSA, a 10-or 100-fold molar excess of the cold probes was added to the EMSA binding reaction.
Acknowledgements The authors thank  H. Ogino  for kindly providing the beta globin-eGFP reporter vector and  J. P Concordet  at  TACGENE (Paris, France)  for providing Cas9 protein. They acknowledge the  ENCODE consortium  and the  ENCODE laboratories of Dr. Bing Ren (USCD)  and  Dr. John Stamatoyannopoulos  ( UW ) for epigenomic data on mouse fetal organs and human cells, respectively. They also acknowledge the laboratory of  Dr. G. Veenstra  for for ChIP-sequencong data on Xenopus embryos and  Dr. I. Ovcharenko  for help with ECR browser. European Union's  Horizon 2020 Research and Innovation Program  supported the research presented in this manuscript, under the  Marie Skłodowska Curie Grant  Agreement No. 642937  (RENALTRACT;  MSCA-ITN-2014-642937 ) and Grants from  CNRS  and  Sorbonne Université.
Data availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Public databases and bioinformatic tools used for our studies are the following : ECR Browser (http:// ecrbr owser. dcode. org/)  24  for comparisons of multiple vertebrate genomes; R-Vista 2. 0 (https:// rvista. dcode. org)  30  to identify conserved putative TFs binding sites; ENCODE databases (https:// www. encod eproj ect. org)  25, 26  to display histones ChIP-seq, DNase-seq and ATAC-seq peaks in mammals and the database from the Veenstra's laboratory for Xenopus (http:// veens tra. scien ce. ru. nl/ track hubx. htm)  27. Accessible chromatin and ChIP-seq data are visualized with the UCSC genome browser at (https:// genome. ucsc. edu/)  76  and Ensembl genome browser (http:// www.
ensem bl. org/)  77. Sequence alignment was performed with the short sequence aligner software ClustalW (http:// www. ebi. ac. uk/ Tools/ msa/ clust alw2/)  78. Gene expression data are available on (https:// sckid ney. flati ronin stitu te. org)  79  , GUDMAP (https:// www. gudmap. org)  80, 81  or Xenbase (https:// www. xenba se. org).
EXPERIMENTAL SETUP Fog is a relatively new concept and hence, one of the main challenges when conducting studies is to have a proper experimental setup. To cope with this difficulty, most works rely on simulated environments which may not reflect the complexity of a real Fog environment. In our work, we first designed a realistic experimental environment to perform our reconfiguration studies. In this section, we briefly present how this setup was built. Note that all the traces and scripts used to generate the figures presented in this document are available at: https://gitlab. inria. fr/demourad/ tpds-journal or https://doi. org/10. 5281/zenodo. 5079486.
The FITOR Platform The Fog IoT Orchestrator  [23] , or FITOR for short, is the base for our experimental setup. FITOR's architecture is depicted in Fig. 4  and it is composed of two main parts: i) the physical infrastructure is presented in the bottom of the figure, detailing the machines used and their connectivity; and ii) the Fog IoT Orchestrator in the top, describing the software components. The source code of the FITOR platform is available at: https://archive. softwareheritage. org/browse/origin/https: //gitlab. inria. fr/demourad/fitor. git/.
Note that in this study, we do not model the Cloud layer. However, FITOR can be easily extended to consider the Cloud by selecting clusters from different locations in France. Unfortunately Grid'5000 is not capable of emulating the characteristics present in the edge of the network. So, for the End Devices/Mist layer we had to rely on another platform, called FIT/IoT-LAB  [25]. FIT/IoT-LAB is an open platform to perform IoT experiments, providing more than 2000 heterogeneous sensors nodes spread in different sites in France. From these nodes, A8 boards form the Mist layer, on which the software stack (IoT application and monitoring tools) runs directly on bare metal due to their constrained capacities. These nodes are generally used for control and have an Ethernet card that allows them to communicate with the Grid'5000 nodes through a VPN.
ACKNOWLEDGEMENTS This work has been done in the context of the  Inria/Orange joint laboratory. Experiments presented in this paper were carried out using the Grid'5000 and the  FIT/IoT-lab testbeds , supported by a scientific interest group hosted by Inria and including  CNRS ,  RENATER  and several Universities as well as other organizations (see https://www. grid5000. frand https: //www. iot-lab. info/).
Finally, we directly compared the official recorded cases in Africa with the ongoing SARS-CoV-2 genomic surveillance data (GISAID date of access: 31 March 2022) for a crude estimation of the variants' contributions to cases. We observe that Delta was responsible for an epidemic wave between May and October 2021 (Fig. 1A ) and had the greatest impact on the continent, with almost 34. 2% of overall infections in Africa possibly attributed to it. Beta was responsible for an epidemic wave at the end of 2020 and beginning of 2021 (Fig. 1A ), with 13. 3% of infections overall attributed to it. Notably, Alpha, despite being predominant in other parts of the world at the beginning of 2021, had only minimal importance in Africa, accounting for just 4. 3% of infections. At the time of writing, the Omicron VOC had contributed to 21.
We show a general trend of decreasing sequencing turnaround time in Africa (fig. S6 ), particularly from a mean of 182 days between October and December 2020 to a mean of 50 days over the same period a year later, although this does come with several caveats. First, we measure sequencing turnaround time in the most accessible manner, which is by comparing the date of sampling of a specimen to the date its sequence was deposited in GISAID. Generally, the genomic data potentially informs the public health response more rapidly than reflected here, particularly when it comes to local outbreak investigations or variant detection. This analysis is also confounded by various factors such as countryto-country variation in these trends (fig. S7 ), delays in data sharing, and potential retrospective sequencing, particularly by countries that joined sequencing efforts at later stages of the pandemic.
Finally, we should also mention uneven sequencing and reporting standards across the different laboratories on the continent-and globally, for that matter. Different groups use different measures for what constitutes a highquality sequence (e. g. , 70 versus 80% sequence coverage) or use different sequencing depth coverage. This lack of global standardization complicates the direct comparison of sequences that may have been submitted to GISIAD using different criteria, further biasing any inference. Given the sheer size of SARS-CoV-2 sequencing, with ~10 million whole-genome sequences shared on the GISAID database (date of access: 31 March 2022), there is an urgent need for global standards with regard to sequence quality and associated metadata. Africa needs to continue expanding genomic sequencing technologies on the continent in conjunction with diagnostic capabilities.
Ethics statement This project relied on sequence data and associated metadata that are publicly shared by the GISAID data repository and adhere to the terms and conditions laid out by GISAID  (16). The African samples processed in this study were obtained anonymously from material exceeding the routine diagnosis of SARS-CoV-2 in African public and private health laboratories. Individual institutional review board references or material transfer agreements (MTAs) for countries are as follows: Angola (MTA -CON8260); Botswana-genomic surveillance in Botswana was approved by the Health.
Epidemiological and genomic data dynamics We analyzed trends in daily numbers of cases of SARS-CoV-2 in Africa up to 31 March 2022 from publicly released data provided by the Our World in Data repository for the continent of Africa (https://github. com/owid/covid-19-data/ tree/master/public/data) as a whole and for individual countries (2). To provide a comparable view of epidemiological dynamics over time in various countries, the variable under primary consideration for Fig. 1  was "new cases per million (smoothed). " To calculate the genomic sampling proportion and frequency for each country for Fig. 2 , the total number of recorded cases as of 31 March 2022 was considered, as well as the total length of time for which each country had recorded cases of SARS-CoV-2. Genomic metadata was downloaded for all African entries on GISAID for the same time period (date of access: 31 March 2022).
From this, information extracted from all entries for this study included the date of sampling, country of sampling, viral lineage and clade, originating laboratory, sequencing laboratory, and date of submission to the GISAID database. The geographical locations of the originating and sequencing laboratories were manually curated. Sequences originating and sequenced in the same country were defined as locally sequenced, irrespective of specific laboratory or finer location. Sequences originating in one African country and sequenced in another were defined as sequenced within regional sequencing networks. Sequences sequenced in a location not within Africa were labeled as sequenced outside Africa. Sequencing turnaround time was defined as the number of days that had elapsed from specimen collection to sequence submission to GISAID.
Primer choice and sequencing outcomes All SARS-CoV-2 genomes from African countries were retrieved from GISAID  (16)  for submission dates from 1 December 2019 to 31 March 2022, yielding 100,470 entries. Associated metadata for the entries were also retrieved, including collection date, submission date, country, viral strain, and sequencing technology. Data on the primers used for the sequencing were requested from investigators and yielded primer data for 13,973 of the entries (~13%). The total N (bases with low sequence depth) per genome were counted, the results of which were then used for genome quality analysis and visualization. Gap locations in the genomes were mapped and visualized with respect to the original Wuhan strain  (64).
Phylogenetic investigation All African sequences on the GISAID sequence database  (16)  were downloaded on 31 March 2022 (n = 100,470). Of these, Alpha accounted for 3851 sequences, Beta accounted for 14,548 sequences, Delta accounted for 35,027 sequences, Omicron accounted for 21,708 sequences, and 25,336 sequences were classified as non-VOCs. Before any phylogenetic inference, we performed some quality assessment on the sequences to exclude incomplete or problematic sequences as well as sequences lacking complete metadata. Briefly, all African sequences were passed through the NextClade analysis pipeline (65) to identify and exclude (i) sequences missing >10% of the SARS-CoV-2 genome, (ii) sequences that deviate by >70 nucleotides from the Wuhan reference strain, (iii) sequences with >10 ambiguous bases, (iv) clustered mutations, and (v) sequences flagged with private mutations by NextClade.
Additionally, Omicron variants were screened for traces of viral recombination with RDP5. 23 (66) using default settings and a p value of ≤0. 05 as evidence of recombination. A large number of sequences were removed (n = 57,421), with incomplete sequences (<90% genome coverage) being the biggest contributor. This produced a final African dataset of 43,049 high-quality African sequences. Because of the sheer size of the dataset, we opted to perform independent phylogenetic inferences on the main VOCs (Alpha, Beta, Delta, and Omicron BA. 1 and BA. 2) that have spread on the African continent, as well as a separate inference for all non-VOC SARS-CoV-2 sequences. To evaluate the spread of the virus on the African continent, we aligned the African datasets against a large number of globally representative sequences from around the world.
Because of the oversampling of some variants or lineages, we performed a random down sampling while retaining the oldest two known variants from each country. Reference sequences were respectively aligned with their African counterparts independently with NextAlign  (65). Each of the alignments was then used to infer maximum likelihood (ML) tree topologies in FastTree v 2. 0 (67) using the general time reversible model of nucleotide substitution and a total of 100 bootstrap replicates  (68). The resulting ML tree topologies were first inspected in TempEst  (69)  to identify any sequences that deviate more than 0. 0001 from the residual mean. After the removal of potential outliers in R with the ape package (70), the resulting ML trees were then transformed into timecalibrated phylogenies in TreeTime (71) by applying a rate of 8 × 10 -4 substitutions per site per year  (72)  to transform the branches into units of calendar time.
Time-calibrated trees were then visualized, along with associated metadata, in R using ggtree  (73)  and other packages. We performed a basic viral dispersal analysis for each of the VOCs (excluding Gamma) as well as for the non-VOC dataset. Briefly, a migration model was fitted to each of the timecalibrated tree topologies in TreeTime, mapping the country location of sampled sequences to the external tips of the trees. The mugration model of TreeTime also infers the most likely location for internal nodes in the trees. Using a custom python script, we could then count the number of state changes by iterating over each phylogeny from the root to the external tips. We count state changes when an internal node transitions from one country to a different country in the resulting child node or tip(s).
The timing of transition events is then recorded, which serves as the estimated import or export event. To infer some confidence around these estimates, we performed 10 replicates for each of the datasets by random selection from the 100 bootstrap trees. Because of the high uncertainty in the inferred locations for deep internal nodes in the trees, we truncated state changes to the earliest date of sampling in each dataset. All data analytics were performed using custom python and R scripts, and the results were visualized using the ggplot libraries  (74). Such phylogeographic methods are always subject to uneven sampling through time (i. e. , over the course of the pandemic) and through space (by sampling location). To address this, we have performed a case-sensitive analysis to investigate the effects of oversampling African locations on the inferred number of viral introductions.
Case-sensitive phylogeographic inference To address the potential oversampling of African sequences relative to global reference in the above-mentioned analyses, we performed another phylogeographic inference on subsamples based on global case counts to try to eliminate oversampling bias in our inference. To this end, we considered all high-quality sequences for each of the VOCs (Alpha, Beta, Delta, and Omicron BA. 1 and BA. 2) globally over the same sampling period (until 31 March 2022). We used subsampler (https://github. com/andersonbrito/ subsampler) to generate subsamples for each variant based on globally reported cases. In short, subsampler uses a case-count matrix of daily cases, along with the fasta sequences and GISAID associated metadata, to sample a user-defined number of sequences.
For each VOC and for BA. 1 and BA. 2, we performed 10 samplings using different number seeds to sample datasets of ~20,000. Once again, sampled sequences were screened for viral recombination as described above and sequences with signs of recombination were removed. Subsampler has the added advantage that it disregards poor quality sequences (e. g. , <90% coverage) and sequences with missing metadata (e. g. , exact date of sampling). Each dataset was then subjected to the same analytical pipeline as mentioned above to infer the viral transitions between Africa and the rest of the world.