Viruses are a significant player in many biosphere and human ecosystems, but most signals remain “hidden” in metagenomic/metatranscriptomic sequence datasets due to the lack of universal gene markers, database representatives, and insufficiently advanced identification tools.Caudovirales). Among the tools evaluated, VirSorter2 was also uniquely able to minimize errors associated with atypical cellular sequences including eukaryotic genomes and plasmids. Finally, as the virosphere exploration unravels novel viral sequences, VirSorter2’s modular design makes it inherently able to expand to new types of viruses via the design of new classifiers to maintain maximal sensitivity and specificity.Here, we introduce VirSorter2, a DNA and RNA virus identification tool that leverages genome-informed database advances across a collection of customized automatic classifiers to improve the accuracy and range of virus sequence detection. When benchmarked against genomes from both isolated and uncultivated viruses, VirSorter2 uniquely performed consistently with high accuracy (F1-score > 0.8) across viral diversity, while all other tools under-detected viruses outside of the group most represented in reference databases , and VirSorter2 is also available both on bioconda and as an iVirus app on CyVerse (https://de.cyverse.org/de).With multi-classifier and modular design, VirSorter2 demonstrates higher overall accuracy across major viral groups and will advance our knowledge of virus evolution, diversity, and virus-microbe interaction in various ecosystems. Source code of VirSorter2 is freely available (Video abstractThe online version contains supplementary material available at 10.1186/s40168-020-00990-y. These efforts will rely on automated detection of viral genomes across a broad range of sequencing datasets . Currentes order , 32, butes order , RNA vires order , 37, andes order , 39. Onees order . While sCaudovirales , while Caudovirales ; RNA-dependent RNA polymerase (RdRP) for the viral kingdom Orthornavirae (RNA viruses); MCP for viruses in the family Microviridae; replication-associated protein for viruses in the phylum Cressdnaviricota [Inoviridae; MCP, pATPase, primase, transcription elongation factor (TFIIS), and viral late gene transcription factor (VLTF3) for viruses in the phylum Nucleocytoviricota ; and MCP for viruses in the family Lavidaviridae (virophages) [Viral sequence identification in VirSorter2 occurs via three steps: (i) input sequences are automatically annotated and relevant features are extracted, (ii) each sequence is scored independently using a set of classifiers customized for individual viral groups, and (iii) these scores are aggregated into a single prediction provided to the user Fig. . Annotatviricota ; ATPase ophages) .Fig. 1ODifferent features were extracted from these annotations Table . These fCaudovirales) and comparable dsDNA phages , (ii) NCLDVs, (iii) RNA viruses, (iv) ssDNA viruses, and (v) lavivirids (Lavidaviridae). Separately, a negative  dataset was generated from genomes of three groups: (i) bacteria and archaea, (ii) eukaryotes (fungi and protozoa), and (iii) plasmids. The training and testing sets for non-viral genomes were generated the same way as the viral set described above.To train the different classifiers, reference genomes for each viral group were split into training and testing sets with a 9 to 1 ratio, respectively, and 5 random fragments were generated from each genome starting at a random position and with size ranging from 1 kb to complete genomes. Separate classifiers were trained for the following viral groups (i) caudovirids , the score is considered significant and the sequence was considered as entirely or near-entirely viral. To identify potential host regions on the edge of these contigs, sub-sequences of the input sequence with 0 to 5 genes or 10% of total genes (whichever larger) trimmed in 5′ and/or 3′ processed with the same classifiers, and selected if the viralness score increased compared to the full sequence. If no score was significant when analyzing the complete sequence, the same classifiers are applied to sliding windows across the input sequence. The size of the sliding window was determined separately for each classifier as the minimal size of a genome from the corresponding viral group Table . The sliCaudovirales. As described above, test sequences included both phage isolate sequences from NCBI Viral RefSeq [We first evaluated the new VirSorter2 approach on genomes in the order l RefSeq  and uncul RefSeq , 16, 48,l RefSeq , VirFindl RefSeq , DeepVirl RefSeq , MARVEL l RefSeq , and VIBl RefSeq . The ovel RefSeq .Caudovirales from RefSeq, as they all displayed F-score ≥ 0.8 with sequences 5 kb and longer —as described above.We next reasoned that, since different viral groups have different defining characteristics, a single model was unlikely to efficiently handle the entire viral diversity. Hence, just as viral taxonomists have to use different characteristics to classify viruses across the known virosphere , differeCaudovirales viruses demonstrated that most of the tools evaluated were not able to consistently identify these sequences  datasets and broad availability for users regardless of their knowledge of command-line or scripting are desirable traits in virus sequence detection tools. For the former, VirSorter2 can process an input dataset of 100,000 sequences totaling 4.2 Gb in 28.6 h using 32 threads. Its memory usage stays nearly constant with increasing data size, and scales nearly linearly with threads used Figs.  & S9. Ithttps://bitbucket.org/MAVERICLab/VirSorter2), as an App on the CyVerse (https://de.cyverse.org/de), and available for local installation through a bioconda package (https://anaconda.org/bioconda/virsorter). In addition, we provide an extensive step-by-step tutorial on how to run VirSorter2 and how to design new classifiers for additional viral groups and integrate this within a local VirSorter2 instance on protocols.io/VERVENET (https://www.protocols.io/view/getting-started-with-virsorter2-bhdij24e).In order to make VirSorter2 useful for the largest community possible, we provide it through multiple implementations and accompanied with extended documentation. Specifically, VirSorter2 is freely available at bitbucket  played by all viruses across Earth’s biomes, and better understand how these viruses constrain fundamental microbial processes.The automatic extraction of viral sequences from large-scale sequencing data is now a cornerstone of the current viral ecogenomics toolkit. Identifying viral genomes from omics data enables unprecedented studies of viral taxonomic diversity , viral phttps://de.cyverse.org/dl/d/6489360D-1126-413B-A667-D18E39D5F2F1/viral_db_default.hmm). Xfams were generated from a large collection of viral sequences from the Global Ocean Viromes 2.0 (GOV 2.0) [p value of < 0.05 in DeepVirFinder), were kept. Open reading frames (ORF) were predicted using prodigal with “--meta” mode [https://de.cyverse.org/de/): /iplant/home/shared/iVirus/Xfams/version_0.5/Xfams-XC.The viral HMM database includes viral HMMs from viral protein families (VPF) of JGI earth’s virome project  and XfamGOV 2.0)  and the GOV 2.0) . BrieflyGOV 2.0) , DeepVirGOV 2.0) , and MARGOV 2.0) . The intta” mode , and theftp.ncbi.nlm.nih.gov/genomes/refseq) on 2020-01-12. Viral genomes from the order Caudovirales were used for dsDNA phage; genomes from the families Mimiviridae, Phycodnaviridae, Faustoviridae, Iridoviridae, Marseilleviridae, Ascoviridae, Pithoviridae, Poxviridae, and Pandoraviridae were used for NCLDV; genomes from the realm Riboviria were used for RNA viruses; genomes from the families Bacilladnaviridae, Circoviridae, Geminiviridae, Nanoviridae Genomoviridae, Parvoviridae, Microviridae, Smacoviridae, Alphasatellitidae, Tolecusatellitidae, Anelloviridae, Bidnaviridae, Pleolipoviridae, Spiraviridae, and Inoviridae were used for ssDNA viruses; genomes from the family Lavidaviridae were used for Lavidaviridae classifier. Other than NCBI RefSeq genomes, a set of high-quality genomes was also obtained from the literature [“Hallmark gene count” is the count of hallmark genes in a viral sequence“% of viral genes” is calculated as the percent of genes annotated as viral  of all genes“% of archaeal genes” is calculated as the percent of genes annotated as archaeal  of all genes“% of bacterial genes” is calculated as the percent of genes annotated as bacterial  of all genes“% of eukaryotic genes” is calculated as the percent of genes annotated as eukaryotic (best hit to eukaryotic HMMs) of all genes“% of mixed genes” is calculated as the percent of genes with best hit to HMMs shared between viruses and cells“% of unaligned genes” is calculated as the percent of genes with no hits to HMMs in the VirSorter2 HMM database (bit score cutoff 30)“Average gene size” is the mean of gene sizes in a viral sequence“Gene overlapping frequency” is the percent of consecutive genes that are on the same strand and overlap when scanning from 5′ to 3′ on the + strand“Gene density” is the average number of genes in every 1000 bp sequence window “Strand switching frequency” is the percent of genes located on the opposite strand from the gene upstream (scanning from 5′ to 3′ in the + strand)“% of ATG as starting codon” is the percent of gene with ATG as starting codon“% of GTG as starting codon” is the percent of gene with ATG as starting codon“% of TTG as starting codon” is the percent of gene with ATG as starting codon“Mean of GC content” is the mean of GC content of all genes in a sequence“SD of GC content” is the standard deviation of GC content of all genes in a sequence“% of RBS motifs” is percent of ribosomal binding sites (RBS) with a specific motif. There are 11 types of motifs included: SD_Canonical, SD_Bacteroidetes, TATATA_3-6, OnlyA, OnlyT, DoubleA, DoubleT, Other_GA, NoA, Other, and None (no motif found) .NCBI RefSeq genomes including archaea, bacteria, protists, fungi, and virus were downloaded from NCBI ; DeepVirFinder was installed directly from GitHub (https://github.com/jessieren/DeepVirFinder) with last commit ID of ddb4a9433132febe5cda39548cb9332669e11427 and was run with default parameters; for both VirFinder and DeepVirFinder, sequences with p value < 0.05 were chosen as viral. Since MARVEL (version 0.2) required each viral genome as a separate file, input sequences were first split so that each sequence was an individual sequence file, and then default parameters were used, and the sequences in “results/phage_bins” were considered as viral. F1 score  was used as a metric for tool accuracy comparisons. For measuring false positives on eukaryotic and plasmid sequences, the fraction of eukaryotic or plasmid sequences classified as viral were calculated. To test performance on provirus detection, 54 bacterial genomes with known provirus boundary [To compare the accuracy of VirSorter2 to other viral identification tools, datasets with equal number (50) of viral and non-viral random DNA fragments were generated from the testing set created above and with different lengths  to discern the impact of sequence length on accuracy. VirSorter2 (version 2.0.beta) was run with “--include-groups dsDNAphage, NCLDV, RNA, ssDNA, lavidaviridae, --min-score 0.5,” and sequences in “final-viral-combined.fa” were considered as viral. VirSorter (version 1.1.0) was run with “--db 2 --virome –diamond,” and sequences in categories 1, 2, 4 and 5 were considered as viral; VIBRANT (version 1.2.1) was run with “--virome -f nucl”, and sequences in “VIBRANT_*/VIBRANT_phages_*/*.phages_combined.fna” were considered as viral; VirFinder (version 1.1) was run with VF.pred function within R as described in .To measure how tools scale with input data size, datasets with 10, 100, and 1000 sequences of 10 kb in length were generated in the same way as in accuracy benchmarking described above. To measure multithreading efficiency, datasets with 1000 sequences of 10 kb were generated and used as an input for VirSorter2, VirSorter, MARVEL, and VIBRANT were run with 1, 2, 4, 8, 16, 32 threads. VirFinder was not included since it did not have a multi-threading option. DeepVirFinder had unexpected behavior with its multi-threading option (“-c”), i.e., the “-c” option could not control thread number, so was also not included. CPU time and run time was measured with “/usr/bin/time -v” command available in the Linux operating system and peak memory was measure with “memusg” script  are generated from genomes in the order Caudovirales in NCBI Viral RefSeq (A), proviruses extracted from microbial genomes in NCBI RefSeq (B) and other sources (C)  are generated from genomes in the order Caudovirales in NCBI Viral RefSeq (A), proviruses extracted from microbial genomes in NCBI RefSeq (B) and other sources (C)  were generated from Caudovirales genomes from both NCBI RefSeq genomes and other sources. Only data sources with > 10 viral sequences that have < 25% genes annotated as viral were kept. Then equal numbers (50) of viral and non-viral  genome fragments were combined as an input for the tested tools. F1 score is used as the metric (y-axis) to compare tools. vs2 = VirSorter2; vs1 = VirSorter; vf = VirFinder; dvf = DeepVirFinder; mv = MARVEL; vb = VIBRANT. Figure S4. Importance of different features for viral sequence identification across viral groups. The y-axis shows the relative contribution of individual features in separating the training viral and nonviral  data , provided by the Random Forest classifier after processing training data, and based on the F1 score. Top four features from each viral group  are shown. In the features (color), “vir”  is calculated as the percent of genes annotated as viral  of all genes; “bac”  is calculated as the percent of genes annotated as bacterial  of all genes; “hallmark”  is the count of hallmark genes in a viral sequence; “mix” (% of mixed genes) is calculated as the percent of genes with best hit to HMMs not specific to virus or non-virus; “Strand_switch_perc” (Strand switching frequency) is the percent of genes located on a different strand from the previous gene (scanning from 5′ to 3′ in the + strand); “density” (Gene density) is the average number of genes in every 1000 bp sequence ; “gc_mean” (Mean GC content) is the mean of GC content of all genes in a contig; “atg_perc” (% of ATG as start codon) is the percent of genes with ATG as a starting codon; “rbs_None” is the percent of ribosomal binding sites (RBS) with no motif detected; “rbs_TATATA_3-6” is the percent of RBS with “TATATA_3-6” motif. Figure S5. Recall comparisons of tools on different viral groups (other than dsDNA phage) from different data sources. Genome fragments of different lengths (x-axis) are generated from NCBI RefSeq (“refseq”) genomes in each viral group and other sources (“non-refseq”). Then equal numbers (50) of viral and non-viral  genome fragments were combined as input for tools. Recall was used as the metric (y-axis) to compare tools. The dotted horizontal line is y = 0.8. vs2 = VirSorter2; vs1 = VirSorter; vf = VirFinder; dvf = DeepVirFinder; mv = MARVEL; vb = VIBRANT. Figure S6. Precision comparisons of tools on different viral groups (other than dsDNA phage) from different data sources. Genome fragments of different lengths (x-axis) are generated from NCBI RefSeq (“refseq”) genomes in each viral group and other sources (“non-refseq”). Then equal numbers (50) of viral and non-viral  genome fragments were combined as input for tools. Precision was used as the metric (y-axis) to compare tools. The dotted horizontal line is y = 0.8. vs2 = VirSorter2; vs1 = VirSorter; vf = VirFinder; dvf = DeepVirFinder; mv = MARVEL; vb = VIBRANT. Figure S7. False positives by VirSorter2 on eukaryotes and plasmids. Genome fragments (50) of different lengths (x-axis) are generated from eukaryotic genomes (fungi and protozoa) in NCBI RefSeq, and plasmids. Percent of genome fragments classified as viral was used as the metric (y-axis) to evaluate tools. Plot A and C show contribution of each classifier (color) to total false positives in VirSorter2  shows all tools scale nearly linearly with data size, and (B) shows peak memory usage of all tools are <1 GB. VirSorter2 and VirFinder peak memory usage stay nearly constant. vs2 = VirSorter2; vs1 = VirSorter; vf = VirFinder; dvf = DeepVirFinder; mv = MARVEL; vb = VIBRANT. Figure S9. Multi-threading efficiency comparison among tools. Tools were run on 1000 sequences with 10 kb in length. Plot (A) shows VirSorter2 and VIBRANT have the best multi-threading efficiency, i.e. total run time decreases nearly linearly with the number of threads used. VirSorter can not use more than four threads. MARVEL’s multi-threading option does not significantly reduce run time. Plot (B) shows VirSorter2, VirSorter and VIBRANT memory usage increases with the number of threads used, with VirSorter2 and VIBRANT increasing at a higher rate than VirSorter. MARVEL’s memory usage stayed constant. vs2 = VirSorter2; vs1 = VirSorter; mv = MARVEL; vb = VIBRANT. Figure S10. Overview of VirSorter2 results for Tara Oceans virome 85_SRF. A. Detection of viral contigs via VirSorter 1.0.4 and VirSorter 2.0.beta by contig size. The top panel displays the total number of viral contigs identified in each size class, while the bottom panel indicates the overlap between these predictions. B. Distribution of VirSorter 2.0.beta score  for Tara Oceans virome 85_SRF sequences, according to the confidence category estimated by VirSorter1 (x-axis). “NA” indicates contigs that were not detected as viral by VirSorter1. VirSorter 2.0.beta detections were based on a minimum score cutoff of 0.5. C. Proportion of sequences from Tara Oceans virome 85_SRF detected as viral based on the dsDNAphage, RNA, and/or ssDNA model(s) (red) or detected based on the NCLDV or Lavidaviridae classifiers only, by size class (x-axis). Table S1. Summary statistics of the genomes used for training VirSorter2 classifiers of different viral groups from RefSeq and non-RefSeq sources. “Genome #” is the number of genomes. “Min size”, “Median size”, and “Max size” are the minimum, median, and maximum of genome sizes per each viral group.Additional file 2. Case-study: identifying viral contig from a Tara Oceans virome dataset Additional file 3. First 20 rows of the “final-viral-score.tsv” VirSorter2 output file for Tara Oceans virome 85_SRF. VirSorter 2.0.beta was used with default parameters, including all classifiers and a minimum score cutoff of 0.5. The columns include first the sequence name, the score for each of the 5 classifiers, the maximum score for this sequence, the group yielding this maximum score, the sequence length, the number of hallmark gene(s) for the maximum score group, and the percentage of viral and cellular genes.Additional file 4. Manual inspection of contigs newly identified by VirSorter2 in Tara Oceans virome 85_SRF. The spreadsheet includes four tabs, including the list and characteristics of contigs ≥ 5kb selected for manual inspection , the DRAM-v annotation of these contigs (“Contigs ≥ 5kb - DRAM-v annotation”), the list and characteristics of contigs ≥ 5kb selected for manual inspection , and their DRAM-v annotation (“Contigs < 5kb - DRAM-v annotation”). For the manual inspection tabs, column headers correspond to the standard output of VirSorter2 , i.e. sequence name, score for each of the 5 classifiers, maximum score for this sequence, group yielding this maximum score, sequence length, number of hallmark gene(s) for the maximum score group, percentage of viral and cellular genes, along with an additional column  indicating the conclusions from the expert curators. The DRAM-v annotation tabs column headers correspond to the default DRAM-v output.


Sarcina (family Clostridiaceae) are associated with gastric dilation and emphysematous gastritis. However, the potential roles of sarcinae as commensals or pathogens remain unclear. Here, we investigate a lethal disease of unknown etiology that affects sanctuary chimpanzees (Pan troglodytes verus) in Sierra Leone. The disease, which we have named “epizootic neurologic and gastroenteric syndrome” (ENGS), is characterized by neurologic and gastrointestinal signs and results in death of the animals, even after medical treatment. Using a case-control study design, we show that ENGS is strongly associated with Sarcina infection. The microorganism is distinct from Sarcina ventriculi and other known members of its genus, based on bacterial morphology and growth characteristics. Whole-genome sequencing confirms this distinction and reveals the presence of genetic features that may account for the unusual virulence of the bacterium. Therefore, we propose that this organism be considered the representative of a new species, named “Candidatus Sarcina troglodytae”. Our results suggest that a heretofore unrecognized complex of related sarcinae likely exists, some of which may be highly virulent. However, the potential role of “Ca. S. troglodytae” in the etiology of ENGS, alone or in combination with other factors, remains a topic for future research.Human and animal infections with bacteria of the genus  Sarcina are associated with gastric diseases of unclear etiology. Here, Owens et al. show that infection with a distinct Sarcina species is strongly associated with a lethal disease that affects sanctuary chimpanzees in Sierra Leone.Infections with bacteria of the genus  Pathogens can emerge due to the acquisition of virulence factors through genetic mutation and horizontal gene transfer5 and due to ecological processes that alter their epidemiology/epizootiology and host range8. These processes are accelerating due to factors such as agricultural intensification9, demographic shifts10, ecosystem perturbations11, geophysical processes12, and global environmental changes13. Furthermore, improved diagnostic methods facilitate detection and characterization of new pathogens and the genetic features that contribute to their emergent phenotypes16.Emerging pathogens pose a substantial risk to animal and human health18. For example, one of the main causative agents of human malaria, Plasmodium falciparum, once thought to have co-evolved with humans, actually arose from a recent zoonotic transmission from a western gorilla )21. Nowhere are such risks more evident than in zoological and sanctuary settings, where captive and semi-captive primates come into frequent close contact with people23. For example, contact with New World primates led to simian foamy virus transmission to primate workers in Brazil24 and monkeypox virus transmission occurred in staff at a primate sanctuary following a monkeypox outbreak in sanctuary chimpanzees ) in Cameroon25.Emerging pathogens of non-human primates are especially salient examples of this phenomenon because of the high potential for such pathogens to infect humans, who are genetically similar hostsPan troglodytes verus Schwarz, 1934; “chimpanzees” hereafter) in Sierra Leone’s Tacugama Chimpanzee Sanctuary  have suffered from a lethal disease of unknown etiology. Characteristic signs are neurologic  and gastrointestinal , resulting in death even after aggressive medical treatment by staff veterinarians. To date, a total of 56 individuals at this facility have died of this condition, which we have named “epizootic neurologic and gastroenteric syndrome” (ENGS), constituting a medical emergency in this population, which averages 93 chimpanzees at any given time. TCS is the largest repository of Sierra Leone’s chimpanzee genetic diversity, a training site for conservationists throughout western Africa, an educational/ecotourism destination important for the local economy, and the only home for displaced or orphaned chimpanzees in the country.Since 2005, western chimpanzees (Picornaviridae: Cardiovirus) infection and toxicity from certain plants  Baill./D. heudelotii (Planch. ex Oliv.) Baill.) were both suspected but, after investigation, deemed unlikely to be causal. In such circumstances where infection has been suspected but known agents have not been identified, diagnostic approaches based on metagenomics have proven useful28. We therefore undertook a case-control epizootiological investigation to identify potential pathogens of all major types  using metagenomics and traditional methods in the TCS chimpanzees, to detect associations between particular microbes and ENGS.Despite ongoing efforts by veterinary staff and international collaborators, the etiology of ENGS has remained elusive. Encephalomyocarditis virus , neutrophilic margination in the microcirculation, moderate to marked gastric dilation, pulmonary edema, acute aspiration of digestive contents, and acute hemorrhage in the thymus, pancreas, or both. In total, post-mortem evaluation documentation was available for 28 chimpanzees, but in only 17 of these was the gastrointestinal tract assessed. Of these 17 patients, 14 had gross evidence of acute gastric dilation, 1 did not show such evidence, and 2 were inconclusive Fig.  and multn = 12), seizures (n = 12), vomiting (n = 10), and abdominal distention (n = 9).We selected 95 archived samples from 32 chimpanzees for analysis  from 2005 to 2018 using standard direct and sedimentation methods32 and previously published primers34 for 12 ENGS cases and 6 controls  protocols38 on 12 ENGS cases and 6 controls  subsequent to an outbreak of respiratory illness in the population. This pathogen was previously documented as a cause of epizootic respiratory disease in wild chimpanzees37, but the clinical features of this rhinovirus C infection (characterized by upper respiratory signs) are not consistent with ENGS.Metagenomics for virus discovery conducted using previously published methodsPCR amplification of the 16S rDNA V4 region was attempted on all 96 samples   belonging to a single OTU belonging to an unknown member of the bacterial family es) Fig. . This OTClostridium (Sarcina) ventriculi from feces of Japanese macaques . Re-examination of all samples by including the Clostridium (Sarcina) ventriculi sequence from GenBank in the Greengenes database demonstrated this organism to be present in all 23 ENGS case samples (Supplementary Data Sarcina is contested (and sometimes the genus name Clostridium is substituted) because Sarcina is phylogenetically situated within the “cluster I” group of Clostridia44, considered the “true” Clostridia, although these organisms are polyphyletic. A proposal was made to change the name Sarcina to Clostridium, but was not approved because the name Sarcina predates the name Clostridium and therefore has priority45.Re-examination of the representative sequence from this OTU against the National Center for Biotechnology Information’s (NCBI’s) GenBank (GenBank hereafter) non-redundant database excluding uncultured organisms identified a putative match (97.2% nucleotide identity) to P = 0.0001, two-tailed; Table S. ventriculi (AF110272) and Clostridium ventriculi DSM286 (NR026146), with the exception of the one variant sequence (1 nucleotide mismatch to the aforementioned published sequences).Oligonucleotide primers specific to the 16S rDNA gene of the unknown organism were successfully developed. PCR with these primers yielded amplicons of the predicted length (289 base pairs [bp]) in 13 of 19 ENGS cases (68.4%) but 0 of 13 controls  plates revealed refractile, cuboid cells in packets, a morphology that is distinctive of members of the genus Sarcina  under the same conditions with ease, including propagating the strain in solid and liquid media, freezing the strain in 10% glycerol, and subsequently recovering the bacterium. On SVGM media, our isolate (JB1) grew more slowly than S. ventriculi “Goodsir” . The JB1 isolate displayed morphology 48, but methylene blue staining showed a cellulose-containing cell wall for isolate JB1 which is not characteristic of S. maxima , yielded a final alignment length of 1585 positions. A maximum-likelihood phylogeny built from this alignment shows isolate JB1 to represent a sister taxon to S. ventriculi, forming a clade with S. maxima, Eubacterium tarantellae, and C. perfringens , we designated it “Candidatus S. troglodytae”. We propose the Candidatus designation in this instance because we were unable to generate a culture suitable for deposition in the requisite two publicly accessible culture repositories in two different countries52.Alignment of 16S rDNA sequences from the “cluster I” group of ens Fig. . Bacteri53 and in silico genome closure. The resulting full, high-quality “Ca. S. troglodytae” assembly consists of a circular chromosome of 2,435,860 base pairs resolved into one single contig and 10 plasmids . We performed whole-genome sequencing using a hybrid approach of mate-pair and shotgun sequencing . The organism shares 96.5% of its open-reading frames (ORFs) with S. ventriculi, with notable differences in sugar pathways and capsule biosynthesis  to ammonia and carbon dioxide57. Bacterial and fungal ureases play a key role in gastrointestinal tract colonization and in chronic human diseases such as gastritis and peptic ulcers58. Additionally, in the extrachromosomal sequences, we found a 34 kb plasmidial prophage containing ORFs for an ATP-binding cassette (ABC) transporter. ABC transporters use ATP to move specific substrates across a cellular membrane and can function either as an importer  or an exporter 59. ABC importers have been associated with increased bacterial survival during colonization of hosts60 and exporters with bacterial drug-resistance61, both of which may enhance pathogenesis of an organism.We annotated the genome with PATRIC62. However, because of the unusual neurologic disease associated with ENGS, we scanned the genome sequence of “Ca. S. troglodytae” for ORFs with sequence homology to known virulence genes using ShortBRED63 and a customized version of the Virulence Factor Database (VFDB)64, but found no evidence of such genes. Using the Comprehensive Antibiotic Resistance Database (CARD)65, we identified two antibiotic resistance ORFs, OXA-241 on the chromosome and salA on plasmid 1, which confer resistance to carbapenems (OXA-241) and lincosamides and streptogramins 67.Sarcinae are not known to produce toxinsCandidatus Sarcina troglodytae” is a proposed member of the established genus Sarcina, most closely related to S. ventriculi, as determined by full-length 16S rDNA phylogenetic analysis. It is an uncultivated, Gram-positive coccus with a tetrad structure and slightly flattened cell morphology and may be identified using the PCR primers: TacuSarc_Diag_F: 5′-TGAAAGGCATCTTTTAACAATCAAAG-3′ and TacuSarc_Diag_R: 5′-TACCGTCATTATCGTCCCTAAA-3′ or the full genome sequence (accessions CP051754–CP051764). We isolated “Ca. S. troglodytae” in an anerobic environment and at mesophilic temperature (37 °C) but were unable to maintain a viable culture for deposition in at least two publicly accessible culture collections, hence the Candidatus status. Samples described here are derived from the brain, liver, and lung tissues of sanctuary western chimpanzees (Pan troglodytes verus) diagnosed with ENGS.“Sarcina within the Clostridiaceae is poorly studied in comparison to the highly studied toxigenic clostridia. In 1842, Goodsir described the type species, S. ventriculi, in the stomach contents of a human patient with recurrent vomiting68. Subsequent studies have provided evidence that bacteria morphologically consistent with S. ventriculi cause abdominal pain, nausea, anorexia, vomiting, hematemesis, dysphagia, diarrhea, and generalized weakness in people69, with esophagitis70 and duodenitis71 noted surgically or as a post-mortem finding72. Morphologically indistinguishable bacteria assumed to be S. ventriculi have also been associated with abomasal bloat in young pre-ruminant animals75, characterized by sudden onset of anorexia, abdominal discomfort, lethargy, dehydration, and shock culminating in high lethality (75–100%) despite treatment76. Gastric dilation in monogastric animals  has also been linked to putative S. ventriculi infection78.The genus Ca. S. troglodytae”, and ENGS, a protracted lethal epizootic syndrome in sanctuary chimpanzees in Sierra Leone. We designed a case-control epizootiological study using a case definition that encompassed the range of clinical presentations associated with the syndrome, which included both sudden death and gastrointestinal and neurologic signs prior to death. This case definition will likely become more refined as the syndrome is studied further. By applying metabarcoding and metagenomics, we did not find differences between case and control groups with respect to infection with any parasite or virus and therefore deemed these types of organisms unlikely to be causes of ENGS. However, bacterial metabarcoding and a subsequent PCR revealed infection with “Ca. S. troglodytae” in 68.4% of ENGS cases but no controls. In one instance, a chimpanzee was PCR-negative for “Ca. S. troglodytae” when healthy but subsequently became PCR-positive after succumbing to ENGS.Our results demonstrate a statistically significant association between a bacterium, “47. Despite being studied since the 1800s, sarcinae have been isolated successfully from only a handful of animal or human sources80  are common to diverse diseases, they are all consistent with emphysematous gastroenteritis, as is acute lethality, which likely results from irreversible hemodynamic instability and resulting systemic shock in emphysematous gastritis cases103.In some cases of Ca. S. troglodytae” not only in the gastrointestinal tracts of affected individuals, but also in internal organs, including the brain. For DNA extraction and culture, all tissues were maintained on dry ice, carefully sectioned using sterile technique, and subsampled from the innermost area while still frozen, leading us to conclude that our findings are not likely due to environmental contamination and instead reflect true infection. In human cases of S. ventriculi infection, there is precedent for bacteremia, likely originating from gastrointestinal translocation105, but to our knowledge presence of viable sarcinae in the central nervous system has not been previously reported. Central nervous system colonization may therefore be an overlooked clinical feature of severe Sarcina infection, or “Ca. S. troglodytae” may be a particularly virulent bacterium within the genus Sarcina. We advocate that prior and future human and animal cases of severe disease associated with sarcinae, particularly those cases without clear predisposing factors, be revisited, as they could represent other heretofore unrecognized presentations of infection with Sarcina bacteria. We also speculate that many documented cases of infection that were assumed to be caused by S. ventriculi based on morphology alone may actually have been caused by infection with taxonomically distinct sarcinae. If so, the genus Sarcina may contain a complex of morphologically cryptic species varying from benign environmental bacteria to lethal pathogens.Notably, we found “Ca. S. troglodytae” remain unexplained. For example, epizootiologically, ENGS incidence peaks in March each year. As with other disease-associated clostridia, sarcinae form environmentally stable spores106 and may be ubiquitous in soil107, but environmental factors may contribute to germination of spores and overgrowth. Seasonal changes known to be important in chimpanzees, such as habitat use, diet (including exposure to plant or arthropod toxins), and physiological condition108, may increase infection risk at certain times of the year. Alternatively, “Ca. S. troglodytae” may be re-introduced seasonally, for instance by migratory animals109, perhaps with seasonal weather patterns facilitating its establishment110. The potential role of “Ca. S. troglodytae” in the etiology of ENGS, alone or in combination with other factors, remains a topic for future research.Many questions regarding ENGS and “Due to lack of infrastructure at TCS and limitations on sample shipments to the U.S., our analysis could only include samples obtained between 2013 and 2016, even though ENGS was first noted in 2005. Ideally we would have obtained the same tissues, especially from the gastrointestinal tract, from all cases, in addition to complete medical records and post-mortem examination notes including gross and histological findings. Unfortunately, this was not possible due to the resource-limited setting and resulting opportunistic sampling. Fortunately, the veterinary staff at TCS have begun standardizing sample and record collection for future cases.Ca. S. troglodytae” and ENGS which, due to ethical considerations, cannot be further investigated by experimentation on chimpanzees. Likewise, infection trials in an animal model  would require a pure culture and, as of yet, we are unable to maintain a culture of this bacterium, hence the Candidatus designation52.Practical and ethical considerations preclude collecting invasive samples from sanctuary chimpanzees, so control samples were limited to serum and feces collected at annual health checks and samples collected post-mortem from individuals determined to have died from causes other that ENGS , which are rare at TCS. Furthermore, the evidence presented here supports an association between “111. It is therefore surprising that no human disease similar to ENGS has been reported to date. Should “Ca. S. troglodytae” indeed affect chimpanzees but not humans, it would represent a rare example of such a pathogen112. However, we cannot rule out physiological stressors, diet-related factors, environmental conditions, or other pathogens as predisposing factors that differ between humans and chimpanzees. For example, in the case of C. perfringens-associated enteritis in humans, changes in gastric and intestinal pH, altered nutritional status, and concurrent infection, particularly with intestinal viruses and parasites, can drastically alter clinical outcomes113.We also note that clinically similar cases have not been reported in other captive or wild populations of chimpanzees or other primates. Moreover, despite over 10 years of illness among the TCS chimpanzees, human cases have not been reported, even among personnel with close daily contact with affected individuals. The genetic and physiological similarities between humans and chimpanzees are often cited as predisposing them to cross-species pathogen exchangeSarcina infection in humans have been reported in the peer-reviewed literature since the beginning of the 1900s , amoxicillin (MIC = 0.50 mg/l), amoxicillin–clavulanic acid, piperacillin–tazobactam, imipenem, clindamycin, levofloxacin, rifampicin, vancomycin, and linezolid104.To our knowledge, only 44 cases of 115, cefuroxime and metronidazole88, nafcillin and cefoxitin116, and surgery in some cases, but is associated with 60% lethality103. That we found evidence of two antibiotic resistance genes in our “Ca. S. troglodytae” isolate, a chromosomal OXA-241-like gene involved in carbapenem resistance and a plasmid-associated salA-like gene linked to lincosamide/streptogramin resistance67, is noteworthy, as these findings may influence ENGS treatment decisions. Overgrowth of sarcinae in the stomach appears to predispose patients to clinical disease72; therefore, probiotics, particularly those containing acidophilic organisms, may prove useful for the treatment or prevention of “Ca. S. troglodytae” infections in chimpanzees. For example, probiotics have proven useful for the prevention of C. difficile-related disease in humans118. Finally, autogenous vaccines have proven useful for the prevention of C. perfringens-related disease in animals120. Such an approach could prove useful for the prevention of ENGS if in vitro growth conditions for “Ca. S. troglodytae” can be determined.Treatment of emphysematous gastritis is similarly unstandardized and includes hemodynamic stabilization with intravenous fluids, broad spectrum intravenous antibiotics effective against Gram-negative and anaerobic bacteria, including meropenemS. ventriculi and human disease have increased in the medical literature from 0 articles from 1900‒2000, to 2 articles from 2000‒2010 and 33 articles from 2011—November 2019  and archived samples were only available from a subset of cases that occurred from 2013 to 2016. Samples had been collected by staff veterinarians during routine health checks or during post-mortem examination, and samples were fresh-frozen (at −20 or −80 °C) upon collection in whirl packs or test tubes  sedimentation performed as warranted. Slides were read by trained and experienced staff veterinarians. Data on the occurrence of parasites thus identified were compiled from 155 such analyses conducted from 2005 through 2018, representing 17 ENGS-affected chimpanzees (cases) and 13 apparently healthy chimpanzees (controls).Microscopy for parasite identification was performed at TCS from 2005 to 2018 following standard veterinary protocols32. DNA was extracted from tissue samples  using the DNeasy Blood and Tissue kit  according to manufacturer’s instructions and eluted in 50 µl of buffer AE . Tissue samples were considered appropriate for this analysis based on published literature showing that infections, including with eukaryotes, can be detected in such samples, even when the tissues analyzed are not the anatomic sites of infection125. Primers were used to amplify the V9 region of the 18S rDNA gene and were based on published pan-eukaryotic sequences127 . To reduce host signal, we used the EMP mammal blocking primer EMP_Mmmal_Block: 5′-GCCCGTCGCTACTACCGATTGGII IIITTAGTGAGGCCCT-[C3 Spacer]-3′ (IDT). PCR reactions were carried out in 25 µl volumes containing 0.3 µM of each primer, 1.6 µM of mammal blocking primer, 12.5 μl 2× HotStart ReadyMix , and 25 ng template DNA on a C-1000 thermocycler  with the following cycling conditions: 95 °C for 3 min; 24 cycles of 98 °C for 20 s, 67 °C for 15 s, 62 °C for 30 s, 72 °C for 15 s; and 72 °C for 1 min.Molecular parasitology using metabarcoding was performed with methods modified from the EMPPCR products were purified using the DNA Clean and Concentrator Kit  and eluted in 25 µl of provided elution buffer. From the 25 µl, 5 µl was then used as a template in a 25-µl PCR mix with the Nextera XT Index Kit v2 (Illumina) and limited-cycle PCR for indexing using an annealing temperature of 55 °C with 12 cycles. Products were separated on a 1.5% agarose gel and visualized to confirm band lengths of ~260 bp. Amplicons were then excised from gels and purified using the Zymoclean Gel DNA Recovery Kit (Zymo Research) and eluted in 20 µl of water. Products were quantified using a Qubit fluorometer . Libraries were sequenced on a MiSeq instrument using paired-end 300 ×300 cycle chemistry (Illumina).128. Forward and reverse reads were assembled into paired contigs using the command multiple_join_paired_ends.py and quality filtered using the command multiple_split_libraries_fastq.py with default parameters, except for setting the Phred threshold to 30 or higher (-q 29) and minimum length to 100 bp (-l 100). Chimeras were identified with Usearch v.6.1129 and removed. Reads were then assigned to OTUs using the QIIME protocol for open reference OTU picking with the command pick_open_reference_otus.py and the default UCLUST tool (v.0.2.0)129, and taxonomy was assigned to OTUs using default settings with the command assign_taxonomy.py against the SILVA database v. 132130. Still-undetermined OTUs were assigned using BLAST within QIIME (-m blast) against the full GenBank database131 and non-target sequences were then removed by filtering. We processed all data from through all filtering steps after which we removed those samples that represented <0.5% of the total filtered data set from further analyses.Raw reads were processed with the QIIME v.1.9.1 pipeline38. Viral RNA was isolated using a QIAamp MinElute virus spin kit (Qiagen), omitting carrier RNA. Extracted nucleic acids were then converted to double-stranded cDNA using the SuperScript double-stranded cDNA Synthesis Kit  and random hexamers and purified using Ampure XP beads . Approximately 1 ng of DNA was prepared as a library for pair-ended sequencing on a MiSeq instrument  using the Nextera XT DNA Library Prep Kit (Illumina). Sequence data were analyzed using CLC Genomics Workbench version 11.0 (Qiagen). In brief, we trimmed low-quality bases , discarded short reads (<75 bp), and subjected the remaining reads to de novo assembly using the CLC assembler with automatic word and bubble size selection and a minimum contig length of 500. We then analyzed contigs for nucleotide-level (blastn) and protein-level (blastx) similarity to known viruses in GenBank. For blastx we applied the BLASTX algorithm with the BLOSUM62 matrix to sequences translated into all six frames. We also analyzed all sequence data at the individual read level by mapping reads to viruses in the GenBank database using the CLC mapping tool at low stringency .Samples were homogenized by bead beating (for solid tissues), clarified by centrifugation, and treated with nucleases 132. PCR reactions were carried out in 25 μl volumes containing 0.3 μM of each primer, 12.5 μl 2× HotStart ReadyMix (KAPA Biosystems), 6.5 μl water, and 25 ng template DNA with the following cycling conditions: 95 °C for 3 min; 30 cycles of 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s; and 72 °C for 5 min. PCR products were then elecrophoresed on 1% low melt agarose gels , excised, purified using a ZR-96 Zymoclean Gel DNA Recovery Kit (Zymo Research), and quantified using a Qubit® Fluorometer (Thermo Fisher Scientific). Equimolar amounts of the barcoded V4 amplicons were pooled and sequenced using a MiSeq 2 × 250 bp v2 kit (Illumina) using custom sequencing primers with 10% PhiX control DNA.Genomic DNA was extracted from solid tissue and blood samples using a DNeasy Blood and Tissue Kit and from fecal, vomit, and stomach content samples using a DNeasy PowerSoil DNA Isolation Kit (Qiagen) according to manufacturer’s instructions. The V4 region of the bacterial 16S rRNA gene was amplified using the following primers, which contain the Illumina sequencing adapters, an 8-nt indexed barcode sequence, a 10-nt pad sequence, a 2-nt linker, and the 16S rRNA V4-specific primer: 16SV4_F: 5′-AATGATACGGCGACCACCGAGATCTACACNNNNNNNNTATGGTAATTGTGTGCCAGCMGCCGCGGTAA-3′ and 16S_V4_R: 5′-GGACTACHVGGGTWTCTAATCCAGTCAGTCAGNNNNNNNNCAAGCAGAAGACGGCATACGAGAT-3′132. Poor quality sequences were removed after paired end sequences were combined into contigs. Sequences were aligned against the SILVA 16S rRNA gene reference alignment database to screen for alignment to the correct region. Preclustering was performed to reduce error and chimeras were detected and removed using UCHIME v.4.2133. The SILVA database was used to classify bacterial sequences while sequences classifying to mitochondria, cyanobacteria, Eukarya, Archaea, or Fungi were removed along with singletons to streamline analysis. Percent abundance graphs were created in GraphPad Prism v8.4.3 .All sequences were demultiplexed on the Illumina MiSeq and were processed and analyzed using mothur v.1.42.0.Ca. S. troglodytae” 16S rRNA gene: TacuSarc_Diag_F: 5′-TGAAAGGCATCTTTTAACAATCAAAG-3′ (Tm = 52.8 °C) and TacuSarc_Diag_R: 5′-TACCGTCATTATCGTCCCTAAA-3′ (Tm = 53 °C) (IDT). PCR reactions were carried out in 25 µl volumes containing 0.2 µM of each primer, 12.5 µl 2× HotStar Master Mix (Qiagen), 10 µl water, and 25 ng template DNA on a C-1000 thermocycler  with the following cycling conditions: 95 °C for 15 min; 29 cycles of 94 °C for 30 s, 48 °C for 30 s, 72 °C for 30 s; and 72 °C for 10 min. PCR products (289 bp expected length) were then electrophoresed on 1.5% low-melt agarose gels with ethidium bromide and 1 kb plus DNA length standards (BioRad), visualized under UV light, and photographed using a GelDoc XR imager (BioRad). Amplicons were then excised and purified as described above and Sanger sequenced on ABI 3730xl DNA Analyzers  at the University of Wisconsin-Madison Biotechnology Center.PCR primers were designed to the V2–V3 region of the “Liquid samples were pipetted directly onto sterile agar plates and solid tissues  were placed in sterile Petri dishes and minced with two sterile blades until homogenized. 200 µl of pre-reduced thioglycollate medium  was added and the mixture was streaked by inoculating loop onto a 100 mm × 15 mm plate, placed immediately into an Anaerogen Compact anaerobic pouch , sealed, and incubated at 37 °C. For liquid growth media, a sterile 18-gauge needle with 1-ml syringe was used to inoculate stoppered tubes that were incubated anaerobically at 37 °C. Cultures were screened by PCR, and cells were directly visualized and grown at least 10 days before they were deemed negative for growth of the bacterium of interest.S. ventriculi “Goodsir” (ATCC 19633 or ATCC 29068) was obtained from the ATCC  and grown according to ATCC guidelines.For comparison, the type strain U test (two-tailed).Live bacterial cells diluted in sterile water or phosphate buffered saline were placed on glass slides, examined with light microscopy, and imaged immediately. For Gram staining, heat-fixed slides were flooded with crystal violet solution for 1 min, rinsed with water, flooded with iodine solution for 1 min, rinsed with water, flooded with decolorizer solution for 1‒5 s, rinsed with water, counterstained with five drops of safranin solution for 30 s, rinsed with water, and air dried. For methylene blue staining, heat-fixed slides were flooded with 1% aqueous solution of methylene blue for 1 min at room temperature, then washed with distilled water and then air dried. All slides were visualized and photographed at ×400 on a Panthera U microscope with a Moticam 5.0 camera . For cell size measurements, strains JB1 and ATCC 29068 “Goodsir” were plated and grown on SVGM plates under identical conditions for 72 h. Bacterial cells from seven distinct colonies were harvested, blinded to the investigator, and examined as follows: live cells in phosphate buffered saline from five non-overlapping visual fields were captured as above and single-cell diameters were quantified using the circle (3-point) measurement tool in the Images Plus software suite v.2.0 (Motic). Cellular diameters were compared using a Mann–Whitney Tissues for histopathology were collected during post-mortem examination by staff veterinarians and immediately fixed in 4% paraformaldehyde at least overnight, then later dehydrated in alcohol, embedded in paraffin wax, cut into 6-µm sections, stained with hematoxylin and eosin, visualized under a light microscope, and photographed. For direct visualization of brain tissue, a 6-mm biopsy punch  was taken from the interior of the cerebrum, minced with sterile blades, smeared onto a clean glass slide, and immediately imaged as described above.Ca. S. troglodytae” isolate JB1 (1508 bp) was queried against the NCBI 16S ribosomal RNA sequence (Bacteria and Archaea) database using megablast131 with default parameters, and the top 50 results as of 17 July 2019 were downloaded from NCBI’s RefSeq or GenBank . For taxonomic completeness, 73 sequences comprising “cluster I” Clostridia134 as previously published135 were also retrieved from GenBank, and duplicates were removed. The type organism from the closest known clade  was included as an outgroup135. The resulting 98 sequences, plus the “Ca. S. troglodytae” sequence, were aligned using MUSCLE3.8.31136 . To quantify nucleotide-level distances among sequences, a pairwise distance matrix was calculated using MEGA7 v.7.0.96137 with pairwise deletion and 1000 bootstrap replicates to estimate standard errors. The phylogenetic position of “Ca. S. troglodytae” was then inferred with PhyML v. 1.8.1138 using the general time reversible (GTR) substitution model as determined by Smart Model Selection139, and 1000 bootstrapped data sets were used to estimate statistical confidences of clades.The full 16S rDNA sequence from “Ca. S. troglodytae”  from the Comprehensive Antibiotic Resistance Database (CARD v.3.0.0)65 was used for antibiotic resistance gene identification, a customized database from the Virulence Factor Database (VFD)64 was used for virulence factor identification with ShortBRED v.0.9.563, and average nucleotide identity (ANI) calculator (Enveomics v.0.1.3)55 was used to generate the average nucleotide identities.The mate-pair reads were processed with Nxtrim v.0.4.4Further information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


The Fukushima Daiichi nuclear power plant accident caused radioactive contamination of the surrounding area. In addition to annual health checkups, a survey of the effects of low-dose radiation exposure on health among Fukushima Prefecture residents after the accident has been conducted. Despite health literacy (HL) being recognized as essential to health, its association with participation in these checkups and the survey remains unknown. We aimed to describe the HL status of the Fukushima Prefecture residents and to verify the hypothesis that HL is associated with participation in both checkup and survey. In a cross-sectional study, a questionnaire was sent to 2000 randomly sampled Fukushima Prefecture residents; data from 770 individuals were analyzed. Communicative and critical HL were measured using a 5-point scale. Factors associated with participation were examined using logistic regression. The survey’s valid response rate was 38.5%. The average HL score was 3.11 ± 0.81. HL was not associated with checkup or survey participation. Checkup participation was negatively associated with radiation anxiety . The HL of Fukushima Prefecture residents after the accident was relatively lower than that of the Japanese general population, which may be attributed to difference in educational background. The complexities involved in understanding the effects of radiation on the health of residents could explain why no association between HL and participation in a health checkup and survey was observed. Future studies with a longitudinal design should clarify causality between anxiety and checkup participation. HL is rOn March 11, 2011, Japan was affected by the Great East Japan Earthquake. The subsequent tsunami damaged reactors of the Fukushima Daiichi Nuclear Power Plant. This accident resulted in the release of a large amount of radioactive materials into the air, causing widespread problems, not necessarily radiation-related physical health issues, but other physical, psychological, and social difficulties . SpecifiUnder normal circumstances, annual health checkups are required by law in most Japanese communities and companies as part of efforts toward disease detection and prevention. Since participants of health checkups reportedly have a lower risk of mortality than non-participants , particiHowever, in the aftermath of the radiation accident, normal circumstances were disrupted. Consequently, Fukushima Prefecture launched the “Fukushima Health Management Survey (FHMS)” to monitor long-term health of its residents following the disaster and promote their future well-being . FHMS inSeveral previous studies found that demographic factors , socioec22.1This survey targeted 2,000 residents of the Fukushima Prefecture aged 20–79 years. We divided Fukushima Prefecture into 4 areas based on the general regional classification of Aizu, Nakadōri, and Hamadōri areas, as well as the evacuation area  and selected 500 people from each area. The selection was based on a two-stage stratified random sampling method . Nakadōri and Hamadōri areas included local municipalities that were partially in the evacuation area; these were included in the evacuation area. The survey instrument was administrated as an anonymous, self-reporting postal questionnaire  between 2.22.2.1(1)Regular health checkup conducted by a municipality or company,(2)Health checkup other than that in category 1 ,(3)Dosimetry of external exposure via a dosimeter (film badge), and(4)Dosimetry of internal exposure via a whole-body counter(5)Briefing session for thyroid ultrasound examination, and(6)Lecture relevant to radiation organized by municipalities, or lecture on radiation and thyroid by medical doctorsThe following categories of health checkups and surveys were identified as objective variables:Further, of these 6 categories, we identified that categories 1 and 2 were types of checkups (“checkups”), and categories 3 through 6 were types of surveys (“surveys”). Respondents who participated in at least 1 category within each type of test since the radiation accident occurred were categorized as participants; the remaining respondents were non-participants.2.2.2To assess HL, we used the 5-point HL scale developed by Each item was rated on a 5-point scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The individual HL status of each respondent was numerically assessed by obtaining the average scores of all 5 items.2.2.3Respondents were asked to report their age, gender, and their employment status (including those on leave). For area, the respondents were grouped into the evacuation area or “other” areas . Respondents were also asked about self-rated health status; their educational background ; lifestyle habits including exercise, alcohol consumption, and smoking; anxiety about radiation; and experience of discrimination and prejudice. Responders’ self-rated health status was categorized into good  and not good .Regarding exercise habit, respondents were asked, “How many times do you play sports or exercise in a month?” These replies were categorized into no habit (never) and the presence of habit (others). Regarding alcohol consumption, respondents were asked, “Do you drink every day?” The replies were categorized into presence (yes) and absence (no and used to but quit). In addition, concerning smoking status, respondents were asked, “Do you smoke almost every day?” The replies were grouped into current smokers (yes) and non-smokers (no and used to but quit). Respondents’ anxiety about radiation and their experience of discrimination and prejudice were assessed by asking questions selected from a qualitative analysis of descriptions of worry, anxiety, and problems related to radiation exposure experienced by the evacuees in Fukushima Prefecture . To asseTo assess their experience of discrimination and prejudice, they were asked to respond to the following 3 statements: (1) I have had the experience of being discriminated against (or unfairly treated) because I lived in an area that was reported to have high levels of radiation, (2) I try not to tell others that I am a resident of that area as much as possible, and (3) I have experienced conflicts and trouble with my family members over radiation health effects. Respondents were asked to rate their answers to each question ranging from “strongly agree” (4 points) to “strongly disagree” (1 point). The sum of scores for each statement in the radiation anxiety category (4–16 points) and the discrimination and prejudice category (3–12 points) were assessed numerically; higher scores indicated a higher level of agreement .2.3To examine the degree of respondents HL, the score of communicative and critical HL (CCHL) was calculated. The second tertile of the average score for respondents was calculated to be 3.4; they were divided into 2 groups according to this score and those with a score higher than 3.4 were categorized into the high HL group; those with a score of 3.4 or less were categorized into the low HL group. Then, to examine the associated factor of HL in participants, univariate analysis was performed. In addition, to examine the association between HL and possible factors based on a conceptual causal model ; a chi-sp-values were based on two-sided tests and p-values < 0.05 were considered statistically significant. All data were analyzed using IBM SPSS Statistics for Windows, version 21 .To explore the association between possible factors, including HL and participation in “checkups” and “surveys,” multiple logistic regression analysis was performed. HL was assessed using the CCHL score, gender, educational background, exercise habit, drinking habit, smoking habit, radiation anxiety, as well as discrimination and prejudice score as explanatory variables. Age , district (evacuation zone/not evacuation zone), and employment status were treated as covariates. These variables were forced into the logistic regression model. All 3In total, 916 people responded (response rate of 45.8%), of which incomplete or inadequate answers from 146 people were excluded, and we analyzed the remaining 770 responses . Percentage of valid responses out of whole responses was 84.1%. The number of valid respondents  by district was 183 (36.6%) in Hamadōri, 189 (37.8%) in Nakadōri, 230 (46.0%) in Aizu, and 168 (33.6%) in the evacuation zone.The mean score for individual CCHL was 3.11 ± 0.81 (mean ± standard deviation). The mean for items 1 through 5 were 3.50 ± 1.04, 3.16 ± 0.98, 2.96 ± 1.00, 2.87 ± 0.92, and 3.08 ± 0.97, respectively. Comparison of age, educational background, and employment status between groups divided into HL level is shown in Additionally, out of the 770 respondents, 528 (68.6%) participated in “checkups” and 226 (29.4%) participated in “surveys.” Univariate analysis shows that being male, low subjective health, residing in the evacuation zone at the time of the radiation accident, being employed, and low radiation anxiety were positively associated with “checkup” participation : 1.40–3.36] and being employed , but was negatively associated with radiation anxiety  . Further4A previous study reported that health checkup participation was associated with individuals’ positive health beliefs . In the Previous studies that measured HL among the Japanese general population  have repContrary to our hypothesis, our results showed that HL was not associated with participation in either “checkups” or “surveys.” A previous study  also faiAlthough, we found no association with HL level and participation in “checkup” and “survey,” we did find that radiation anxiety was negatively associated with participation in “checkups.” Concerns about radiation risks were reportedly associated with psychological distress among evacuees of the Fukushima nuclear disaster . A previOn the other hand, experiencing radiation anxiety led to a higher participation in “surveys” compared to participation for “checkups,” although this association was not significant. Radiation anxiety was reported to mediate immediate fear/anxiety after the accident leading to psychological distress , which wBeing employed was associated with participation in “checkups,” though not with “surveys.” In this study, some respondents work at companies and undergo health checkup with which their employer are obliged to provide them. The result of association between employment status and participation in “checkups” might have reflected this fact.The increased risk of non-communicable diseases demonstrated the negative effect of the accident on the health of Fukushima Prefecture residents. These diseases appear to be the result of unhealthier lifestyles after the disaster. Residents in Minamisoma City who participated in the checkup reportedly experienced no cardiovascular health effects related to the nuclear accident . Since rAs countermeasures, a previous study reported that instead of large-group and one-way communications, holding small group discussion to listen to participants’ concerns could help build rapport and might be effective in mitigating radiation anxiety . AnotherThis study has some limitations. First, the study design was cross-sectional; therefore, we could not determine any causal relationship between HL and participation, as well as associated factors. Second, the use of a self-reporting postal questionnaire might have caused low response rate, which led to low generalizability and inaccuracy of information as well as sampling bias. Furthermore, examination of generalizability was not possible because we could only collect name and address of non-responders, which made it impossible to compare characteristics between responders and non-responders. Third, data on lifestyle habits and participation in “checkups” before the disaster were not collected from the responders as such, we were unable to compare these variables before and after the disaster, or determine whether changes in lifestyle habits and/or “checkup” participation occurred due to the disaster. Fourth, although economic status represented as income was reported to be associated with participation in “checkups,” no supporting information was collected. Hence, the analysis did not adjust for economic status. A previous study regarding mechanism of effect of income on screening behavior suggested that income level affected the possession of private health insurance, which is related to higher participation in screening . Althoug5We observed that the HL of Fukushima Prefecture residents after the Fukushima Daiichi Nuclear Power Plant accident was lower than that reported previously for the Japanese. The residents’ HL level was not associated with their participation in “checkup” and “survey.” Instead, residing in the evacuation zone, being employed, less radiation anxiety was positively associated with participation in “checkups.” Residing in the evacuation zone at the time of the accident and a high level of education were positively associated with participation in “surveys.” Implementing measures to mitigate radiation anxiety might be beneficial in promoting participation in “checkups.”KAKENHI, 10.13039/501100001691Japan Society for the Promotion of Science (JSPS), as a Grant-in-Aid for Scientific Research (C) research [JSPS KAKENHI grant number 15K08810].This study was supported by a grant from Nobuaki Moriyama: Formal analysis, Writing - original draft. Chihiro Nakayama: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Data curation, Writing - review & editing, Visualization, Supervision, Project administration. Masatsugu Orui: Investigation, Writing - review & editing. Yujiro Kuroda: Investigation, Writing - review & editing. Hajime Iwasa: Writing - review & editing. Teruko Horiuchi: Writing - review & editing. Takeo Nakayama: Funding acquisition, Investigation, Writing - review & editing. Minoru Sugita: Funding acquisition, Investigation, Writing - review & editing. Seiji Yasumura: Conceptualization, Formal analysis, Resources, Data curation, Writing - review & editing, Visualization, Supervision, Project administration, Funding acquisition.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


EEG was recorded during presentation of a non-social video to a cohort of 6-month-old infants and behavioural data was collected at 6- and 9-months-old. Initial analyses replicated the finding that frontal theta power increases over the course of video viewing, extending this to 6-month-olds. Further, individual differences in the magnitude of this change significantly predicted non-verbal cognitive ability measured at 9-months, but not early executive function. Theta change at 6-months-old may therefore be an early indicator of later cognitive ability. This could have important implications for identification of, and interventions for, children at risk of poor cognitive outcomes.Cognitive ability is a key factor that contributes to individual differences in life trajectories. Identifying early neural indicators of later cognitive ability may enable us to better elucidate the mechanisms that shape individual differences, eventually aiding identification of infants with an elevated likelihood of less optimal outcomes. A previous study associated a measure of neural activity (theta EEG) recorded at 12-months with non-verbal cognitive ability at ages two, three and seven in individuals with older siblings with autism . In a pr When searching for early predictors of later cognitive functioning, behavioural measures are often confounded by factors such as language, motor and attention capacities and can be hard to apply across cultures . Neural In this paper, we focus on an analysis of power within the theta band frequency recorded over frontal channels, since increases in theta power  have been strongly associated with learning and memory functions across species . HippocaSimilar results demonstrating an increase in theta power across frontal regions during activities requiring considerable cognitive processing have been found in young children , indicating that theta function may be somewhat analogous across animals, human adults and infants . In lineIn many previous studies, participants completed experimental tasks with different conditions and theta power was compared between these conditions. However, within what are sometimes thought of as ‘baseline’ recordings, changes in frontal theta power in infants have also been found to be informative see , supportThe cognitive mechanisms associated with theta power  remain debated . In partA complement to experimental attempts to dissect the precise cognitive ‘meaning’ of theta oscillations is to examine whether they have predictive validity to later cognitive outcomes. Whilst broad changes in theta power have been associated with information consolidation, few studies have focussed on whether individual differences in theta change or modulation associate with later developmental outcomes. A recent study in a sample consisting primarily of infants with older siblings with autism  found thThe present pre-registered study sought to address these questions. Furthermore, there is a large literature linking fronto-medial theta in adults to cognitive control , as well1.1The present study utilised EEG and behavioural data from a large sample of typically developing infants tested longitudinally , 2018a. We had three aims in this study. First, we examined the robustness of a refined operationalisation of individual differences in frontal theta change . We included two indices of frontal theta change; a measure of change from the first 30 s to the second 30 s of video viewing, and a continuous measure of change throughout the video. The former index is in line with previous work , whilst Second, we tested whether there was a positive predictive relationship between frontal theta power change at 6 months and non-verbal ability, as assessed by the Mullen Scales of Early Learning, American Guidance Service edition  at both 6 and 9 months. To assess early EF, we used the A-not-B task  dataset were pre-registered on the Open Science Framework website. Extensive details on our analysis plan, indices and exclusions can be found in the pre-registration: https://osf.io/v5xrw/. Importantly the EEG analyses were carried out separately by a different team (E. K. B. and E. J. H. J.) from the behavioural testing and data analyses (K.H.) and the two datasets were only combined after the pre-registration had been submitted.Our analyses of the 2.1Parents and infants were recruited from a lab database when infants were 4 months old. Parental informed consent was obtained before participation in the study. The whole cohort included 104  four-month-old infants from the Greater London area, with 94 retained at the 9-month visit. Most visits occurred within one week of infants reaching their 6-/9-month birthday. Of this group, 47 infants provided sufficient artefact-free EEG data at 6 months and 45  of these also had sufficient behavioural data to be included in the analyses. Over three quarters of the sample was of White ethnic background (80 %), 15.6 % were of Mixed ethnic background, 2.2 % were of Asian ethnic background and 2.2 % were of Black ethnic background. 2.22.2.12.2.1.1EEG was recorded in a shielded room using 64-channel sensor nets from Electrical Geodesics Inc. (EGI), referenced to the vertex, digitised at 250 Hz, and band-pass filtered at 0.1–100 Hz.2.2.1.2M = 121.11, SD = 10.65). Video session time as reported here did not take into account whether the infant watched or not, however, separate measures of on-screen looking time were calculated and analysed; these analyses are presented in the Supplementary Materials  and a Fast Fourier Transform (Matlab) was used to extract power in the 3−6 Hz band across fronto-central electrode sites . In accoFor Index A: The first 1 minute of data from the first clean segment of data was used; this was split into two 30-second halves for comparison. Participants were only included if they provided at least five artefact-free segments per first and second 30 s of this period. Inclusion criteria for both indices are in line with previous work . Power vFor Index B: Participants were only included if they provided at least 10 artefact-free segments over the course of the whole video (from the first clean segment). Theta power for each artefact-free segment was calculated by averaging across electrodes within the topographical group as in Index A. These were correlated with segment number using Pearson correlation to establish the rate of theta power change over the course of the video for each infant. Segments were numbered from the first clean segment of data. We first established that Index B was sensitive to the positive association between theta increase and ‘time’ as observed in previous work  and prod2.32.3.12.3.1.1https://psyarxiv.com/psb8f/).The Freeze-Frame task , 2018a w2.3.1.2The Freeze-Frame Inhibitory Control index was the proportion of looks to the distractor across all trials at 6 months and in interesting trials only at 9 months .2.3.22.3.2.1The A-not-B task was administered at 9 months. The version of the A-not-B task used in this study involved an adaptive testing procedure where the hiding location and delay between hiding and search were adjusted depending on the infant’s performance  is an assessment of children’s motor and cognitive development that can be used from birth to five years of age . It cons2.3.3.1Standardised scores for the five scales were calculated according to the manual . Only a 2.4http://osf.io/v5xrw), a one-tailed p-value of less than 0.05 was used to infer statistical signiﬁcance for these confirmatory analyses. Two follow-up exploratory correlation analyses were also carried out. A Bonferroni correction for multiple comparisons was applied for the exploratory analyses such that a two-tailed p-value of less than 0.025 was taken to be suggestive of a significant effect.Correlations were used to assess bivariate associations between frontal theta power change and Freeze-Frame Inhibitory Control score at 6 and 9 months, A-not-B score at 9 months and MSEL non-verbal score at 9 months. Positive associations were predicted in all analyses . In line with the pre-registration (33.1t-test showed that theta power change for each participant was not significantly different from zero t(32) = 1.01, p = 0.321, 95 % CI , M = 0.03, SD = 0.19.To identify the best index of change in frontal theta power, we followed the steps we set out in the pre-registration. Index A  compared average theta power in the first and second 30 s of video viewing , with theta power change calculated as the difference between the two. A one-sample F = 57.37, p < 0.001; adj r2 = 0.333; standardised beta = 0.58; t(113) = 7.57, p < 0.001). The quadratic model explained less variance  = 28.51, p < 0.001), and the quadratic component was not significant  = -0.33, p = 0.75). The use of a linear model was thus supported. t-test was performed on the resulting individual r-values, which revealed that r-values were significantly different from zero t(46) = 4.34, p  < 0.001, d = 0.63, 95 % CI ; r-value was positive , indicating that frontal theta power increased over the course of the video in infants as a group. Since Index B was sensitive to the phenomenon of interest and allowed us to include a greater proportion of infants than Index A, we used Index B for the correlations with cognitive performance measures. Choosing Index B conformed to the procedure outlined in the pre-registration. Using Spearman correlation for Index B (instead of Pearson correlation) yielded nearly identical results to the ones reported in the following . We first established that the linear assumptions underpinning Index B were met by testing the fit of a linear vs a quadratic polynomial on data from the collapsed sample (generated by averaging data from each infant who provided a clean segment within each second of the video and modelling the relation with time for bins with at least 5 segments of clean data). The linear model fit well  = 0.302, n = 42, p = 0.026, 95 % CI , r(36) = 0.09, n = 38, p = 0.289, 95 % CI  and r(38) = 0.09, n = 40, p = 0.288, 95 % CI , or with performance on the A-not-B task at 9 months, r(38) = -0.158, n = 40, p = 0.165, 95 % CI . See The magnitude of association between frontal theta power and time (how long the infant had been presented with the video for) was positively correlated with non-verbal cognitive level at 9 months 3.3r(40) = 0.50, n = 42, p = 0.001, 95 % CI , r(40) = 0.01, n = 42, p = 0.936, 95 % CI , at 9 months.To further investigate the link between frontal theta power change and cognitive ability, we separately examined the sub-components of the MSEL non-verbal cognitive scale: Fine Motor and Visual Reception. A two-tailed Pearson correlation revealed that frontal theta power change at 6 months significantly predicted Visual Reception abilities, 4Though frontal theta power has previously been linked to learning and memory , little The finding that individual differences in frontal theta modulation at 6 months predicts non-verbal cognitive ability at 9 months is consistent with work by may in turn relate to later cognitive ability. Current behavioural measures of cognitive development are often influenced by a number of confounding factors, including the child’s compliance, the testing environment, and what the child has already learnt, which is highly influenced by experience. Cognitive measures in infancy tend to be particularly problematic because infants have limited language, motor and attention skills , a moderate-to-large effect, whereas the equivalent correlation with the Fine Motor scale indicated no association, r = 0.01 (p = 0.94). These findings were exploratory and will therefore need replication in an independent sample. It is interesting to note, however, that the Visual Reception scale measures skills in visual discrimination, memory, organisation and processing, and thus may reflect infants’ abilities to process and consolidate visual information. Since the Visual Reception scale taps primarily non-verbal aspects of cognitive development, such as visuo-spatial abilities and early problem-solving skills, this finding could indicate that frontal theta modulation relates particularly closely to these aspects of development, which may later translate into fluid aspects of intelligence. The relationship between these skills and underlying neural processes such as frontal theta could be a fruitful area for future investigation.Exploratory analyses of the two scales comprising the non-verbal MSEL score, indicated that the effect was specific to infants’ Visual Reception abilities. The correlation between frontal theta change at 6 months and Visual Reception at 9 months was Follow-up analyses indicated that frontal theta change predicted later non-verbal skills over and above both overt visual behaviour and average theta power during video viewing , 2.9. InA strength of the current study is that it was pre-registered on the OSF website, with a clear analysis plan that was followed meticulously to maintain openness and transparency . FurtherWhilst the focus of the current study was on frontal theta power, it is likely that other components of infant EEG correlate with important aspects of development. An extensive literature exists linking alpha power to early EF e.g., , and a rNonetheless, the findings of the present study confirm the feasibility of identifying neural correlates that predict later cognitive ability from early infancy, an important first step for the potential identification of and intervention for delayed cognitive development.https://osf.io/8yhwe/.The data that support the findings of this study are openly available in anonymised form on OSF: Eleanor K. Braithwaite: Conceptualization, Formal analysis, Methodology, Visualization, Writing - original draft, Writing - review & editing. Emily J.H. Jones: Conceptualization, Formal analysis, Methodology, Supervision, Visualization, Writing - review & editing. Mark H. Johnson: Conceptualization, Funding acquisition, Methodology, Supervision, Writing - review & editing. Karla Holmboe: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing - review & editing.The authors whose names are listed above certify that they have no affiliations with or involvement in any organisation or entity with any financial interest , or non-financial interest , in the subject matter or materials discussed in this manuscript.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The corresponding author emails Abdelkrim Hmadchakhmadcha@gmail.com; khmadcha@upo.esBernat Soriabernat.soria@umh.es; bsoresc@acu.upo.esWe further neglected to indicate the funder “Juvenile Diabetes Research Foundation, JDRF 2-SRA-2019-837-S-B I” to Bernat Soria.Affiliations 2 and 3, Bernat Soria should also have an additional affiliation “ISABIAL and Institute of Bioengineering, University Miguel Hernández de Elche, Alicante, Spain.”In the published article, as well as having References. Reference 67 was incorrectly included as “Riera MLS A. A., Stefanov K. S., Tong H., Riera C. L., García-Olmo D., García-Arranz M. Phase Ib Open Clinical Trial to Assess the Safety of Autologous Mesenchymal Stem Cells for the Treatment of Nonrevascularizable Critical Lower Limb Ischemia. J Stem Cell Res Ther. (2017) 7:391.”. Instead, it should be “Riera ML, Salazar AA, Stefanov KS, Tong H, Riera CL, García-Olmo D, et al. Phase Ib open clinical trial to assess the safety of autologous mesenchymal stem cells for the treatment of nonrevascularizable critical lower limb ischemia. J Stem Cell Res Ther. (2017) 7:391. doi: 10.4172/2157-7633.1000391.”Corrections have been made to the Cellular medication as a therapeutic alternative in chronic critical limb ischemia in diabetic patients without the possibility of revascularization (Dissertation Thesis). University of Sevilla, Seville, Spain (2016).”Reference 86 was also included as “Escacena N. Cellular medication as a therapeutic alternative in chronic critical limb ischemia in diabetic patients without the possibility of revascularization. Dissertation Thesis. Sevilla Spain: University of Sevilla. (2016)”. This reference should be included as number 107 “Escacena N. In the original article, there were multiple errors in the text. Removal of various sections is required due to legal issues. Figure 2, Figure 5, and Table 4 require removal.Paragraph 2 in the section “The Use of MSCs” on pages 8 and 9 requires removal.COST OF THE PROCESS” on pages 13 and 14. The section has been revised as follows:A correction has been made to the section ““After the introduction of CAR-T cell therapies with an actual cost of ~300,000 to 400,000 € (125) or the prices charged by PROCHYMAL  or Provenge (an autologous cell therapy of dendritic cells from metastatic forms of prostate cancer), with prices between $100,000 and $200,000 US, it seems absolutely necessary to analyze the cost-effectiveness of a potential treatment to facilitate the universal coverage of healthcare. A recent survey from the International Society for Stem Cell Therapy estimates costs for a dose between 10,000 and 25,000 € (126), with additional costs from hospitalization and the endovascular department, among others, resulting in a total cost of 30,000 to 40,000 € for a single dose. This cost may be assumed for rare diseases with a low prevalence, but it seems quite difficult to extend this treatment to a highly prevalent medical condition. The only way to reduce the cost is the mass production of allogeneic doses and facilitation of administration. Intramuscular administration of allogeneic MSCs will reduce the total cost and may be as effective as the intraarterial route. Given the reported adverse events, such as microthrombosis (19), clot formation , or IBMIThe section “INSTITUTIONAL REVIEW BOARD AND REGULATORY COMPLIANCE” and its sub-sections “Clinical Trials” and “Regulatory and ATMP Manufacturing” on page 15 require removal.As a result, two figures require renumbering. A correction has been made to Figure 3 and Figure 4 on pages 11 and 12. The figures should now state the following:“Figure 2. Instant blood-mediated inflammatory reaction” and “Figure 3. Implications of MSCs for thrombosis risk during cell therapy”.The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.