dataesr/scientific-paragraphs-categorization huggingface dataset:
Duchenne muscular dystrophy patient characterization and cohort composition To perform miRNA profiling in the serum of DMD patients, we collected samples of 100 DMD patients and 123 healthy controls from 10 European medical centres (Supporting Information, Table  S1 ). The study was approved by central and local Institutional Review Board (IRB) and recorded on ClinicalTrials. gov (Identifier: NCT01380964). All patients or parents for minors signed informed consent. Following RNA extraction from the plasma and quality control validation, we selected 81 RNA samples and constituted nine groups of nine patients. The groups consist of DMD patients, either untreated (DMD-UT) or receiving treatment with glucocorticoid (DMD-T) and healthy controls, in three age categories of 4-8, 8-12, or 12-20 years old (Figure  1A ).
The Ingenuity pathway analysis (IPA) package (Qiagenbioinformatics) was used for the algorithmic prediction of host-gene networks, assigning the fold change and P-value of their embedded miRNAs (Supporting Information, Table  S8 ). Three networks were identified, all of which including the terms Lipid Metabolism and Small Molecule Biochemistry (Table  1 ). Of interest, the three networks integrate a number of biomolecules that are known to be involved in DMD pathophysiology, some of which are recognized as therapeutic targets, supporting the relevance of the analysis. It included, in network-1, insulin, MAPK, Erk1/2, PI3K and Akt, all members of the canonical muscle hypertrophy pathway, NF-ĸB, H3/H4 histones, PDGF and PDGFR, Hox9A, and the mediator complex. The SREBP-1 gene was identified as the most connected in the first and largest network (Network 1, Figure  4A ).
Network 2 includes cholesterol,  18  EGFR,  33  αand β-estradiol, and their oestrogen receptor. 34 Beta-estradiol was the most connected molecule in this network (Network 2, Figure  4B ). Network 3 includes TGF-beta and the hepatocyte nuclear factor HNF4a, which is its most connected molecule (Network 3, Figure  4C ). Finally, upon integrating the three networks using the IPA software, the SREBP-1 molecule was the most connected molecule of the combined network (Figure  4D ). This basic helix-loop-helix transcription factor, as well as its family member SREBP-2, can bind specific sterol regulatory element DNA sequences to up-regulate the synthesis of enzymes involved in sterol biosynthesis, thus are master regulators of lipid metabolism. 35 n a complementary approach, the same 71 host genes (Supporting Information, Table  S8 ) were subjected to a gene ontology (GO) enrichment analysis using the ReactomePA.
Particular enrichment was identified for terms that are related to lipid metabolism, including the activation of gene expression by SREBP (at the upper position by P-value), regulation of lipid metabolism by peroxisome proliferator activated receptor alpha (PPARA), COPI mediated transport, regulation of cholesterol biosynthesis by SREBP and metabolism of steroids (Figure  4E ). Considering that a large proportion of host genes are involved in lipid metabolism, the ClusterProfiler R package was used on a sub-selection of the host genes which are known to participate in lipid metabolism. This analysis suggested the involvement of this network in the functions of transport to Golgi, PPARA transcriptional activity and regulation of lipid metabolism, regulation of cholesterol synthesis, COPI mediated anterograde transport, metabolism of fat-soluble vitamins (vitamin D) and of steroids (Figure  4F ).
However, the target genes analysis failed to produce novel hypotheses or an explanation for the DMD pathophysiology. Importantly, among the dysregulated miRNAs, we noticed some positioned inside mRNA transcripts that are known to be dysregulated in the dystrophic muscle in DMD. This observation supports a hypothesis that, in DMD, miRNAs and their host-gene might be coordinately dysregulated, and thus, miRNA dysregulation may indicate a host-gene dysregulation. Taking these considerations into account, we hypothesized that a biological interpretation approach for circulating miRNA that combines both target and host genes, which is described schematically in Supporting Information, Figure  S8 , may provide an improved interpretation of DMD pathophysiology. The IPA-based gene-interaction analysis predicted three dysregulated networks, all of which contain the term lipid metabolism.
Acknowledgements We are grateful to the  In vivo evaluation, Imaging and Histology services of Genethon. We are grateful to the  Myobank , the tissue bank of the, AFM, and the  Myology Institute (Paris, France)  for providing DMD and healthy control skeletal muscle biopsies. This study was financially supported by the  Association Francais contre les Myopathies (AFM) ; by  ADNA (Advanced Diagnostics for New Therapeutic Approaches) ; the  Institut National de la Sante et de la Recherche Medicale (INSERM) ; the  Universite Pierre et Marie Curie Paris 06 ; the  Centre National de la Recherche Scientifique (CNRS). The authors wish to thank patients and parents for participating, and all staff involved in sample collections in the different medical centres. The authors of this manuscript certify that they comply with the ethical guidelines for authorship and publishing in the Journal of Cachexia, Sarcopenia and Muscle.
Conflict of interest L. S. is member of the SAB or has performed consultancy for Sarepta, Dynacure, Santhera, Avexis, Biogen, Cytokinetics and Roche, Audentes Therapeutics and Affinia Therapeutics. T. V. is the Chief Scientific Officer of DiNAQOR AG. He also serves on the data safety monitoring board for trials sponsored by Italfarmaco and Sarepta. He is a consultant for Antisense Therapeutics, BioPhytis, Catabasis, Constant Therapeutics, Italfarmaco, Prosensa, Sarepta, Solid Biosciences and Syneos. All other authors declare no competing interest.
Acknowledgements We would like to thank  G. Schull  for fruitful discussions. A. R. acknowledges support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  771850 ) and the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  894434. P. M. acknowledges support from the  A. v. Humboldt Foundation , the  ERC Synergy Program  (grant no. ERC-2013-SYG-610256 , Nanocosmos),  Spanish MINECO ( MAT2017-85089-C2-1-R ) and the  "Comunidad de Madrid"  for its support to the FotoArt- CM  Project  S2018/NMT-4367  through the  Program of R&D  activities between research groups in Technologies 2013, cofinanced by  European Structural Funds.
There are, however, a number of uncertainties regarding the application of α-emitters, among those, the unfavorably short half-life of bismuth-213 and the complicated decay scheme of actinium-225, which comprises a considerable risk of undesired side effects. The inability of imaging these α-emitters and, finally, the difficult production scenarios make their application challenging  [18] [19] [20]. In 2006, Ginj et al. introduced the concept of SSTR antagonists for targeting NENs  [21]. Preclinical studies demonstrated much higher tumor accumulation of these non-internalizing SST analogues than for SSTR agonists. In a first-in-human clinical application with four patients with metastatic NENs, the antagonist [ 177 Lu]Lu-DOTA-JR11 performed better than [ 177 Lu]Lu-DOTATATE  [22] , which led to a Phase 1/2 multicenter clinical study (NCT02592707).
Assessment of early side effects Early side effects were assessed based on the RBW of the mice. When an endpoint was reached or at the end of the study (at day 49), blood plasma parameters (albumin (ALB), creatinine (CRE), blood urea nitrogen (BUN), alkaline phosphatase (ALP), and total bilirubin (TBIL)) were determined (Supplementary Material). At the same time, relevant organs and tissues were collected, and the masses were put into relation to the brain mass of the respective mouse in order to allow the comparison of organ-to-brain and organ-to-body weight ratios among the single treatment groups (Supplementary Material). Data were analyzed for significance using a one-way ANOVA test with a Tukey's multiple comparisons post-test.
Acknowledgements The authors thank  Dr. Christoph Umbricht ,  Susan Cohrs ,  Fan Sozzi-Guo ,  Raffaella Schmid , and  Patrick Barrit  for assistance of the preclinical experiments and  Dr. Zeynep Talip ,  Chiara Favaretto , and  Colin Hillhouse  for assisting the production of terbium-161 at PSI, Switzerland; they also thank  Lebogang Sepini  for assistance with the target irradiations at Necsa, South Africa.
Study design, participants, and setting This was a cross-sectional study using data from the fourth wave of the Living Condition of Elderly Study in South Korea conducted in 2017. The survey comprised interviewing community-dwelling older adults who agreed to participate. Participants were eligible if they were aged ≥65 years. In the current study, we analyzed the data of participants who were diagnosed with cancer at the time of data collection. Among the 10,299 individuals who participated in the survey, data of 391 individuals who reported having cancer were analyzed. The Institutional Review Board of K University granted ethical approval.
6% of them were in the prefrailty status group. The prevalence of frailty varies by participant characteristics, settings, and the measurements for identifying frailty. A recent systematic review  [9]  reported that frailty and prefrailty are prevalent in an average of 42% (range, 6%-86%) and 43% (range, 13%-79%) of older cancer patients, respectively, with the prevalence varying widely when measured according to comprehensive   [12]  when the measurement used was the same as in the current study. Bennett et al. [8]  reported that 18% of breast cancer survivors aged 70-79 years were frail and 50% were prefrail, in contrast to only 11% of the women without a cancer history in the same age group. Because we targeted community-dwelling participants who have ever been diagnosed with cancer, both cancer survivors and those with active cancer currently treated at the time of data collection were included in the analyses.
In the study presented here, shotgun metagenomic sequencing was performed for both rhizosphere and bulk soil samples collected at a subset of six time points analyzed in the previous study (TPs 3, 4, 8, 9, 10, and 11). These time points span early drought stress (TP3, 4), the peak of a drought (TP8), and subsequent drought recovery (TP9, 10, and 11) (Supplementary Fig. 1 ), and the resulting metagenomic data represent approximately 609 gigabases (Gb) of raw shotgun metagenomic sequence. To determine whether the shotgun metagenomics data reveal similar shifts in bacterial community composition following drought treatment as had been observed previously with amplicon analysis, we compared the taxonomic composition of our published 16S rRNA datasets  6  with taxonomic profiles generated from co-assembled contigs generated from the shotgun dataset (Fig.
1a-d ). As was observed in the 16S rRNA dataset (Fig. 1b ), the shotgun data revealed a large increase in the relative abundance of Actinobacteria under drought stress in the rhizosphere community (Fig. 1d ) and a weak enrichment in the surrounding soil under drought, as compared to control (Supplementary Fig. 4a-d ). In addition, both datasets show that the enrichment of Actinobacteria under drought stress dissipates once the plants are rewatered, which occurred between TP8 and TP9 (Fig. 1b, d ). Finally, the shotgun data corroborate a previously observed enrichment in Actinobacteria in the rhizosphere within the early stages of control-treated sorghum under control (Fig. 1c ). However, in addition to the noted similarities in treatment-dependent longitudinal patterns in the amplicon and co-assembled contig data, several differences were also noted.
For example, irrespective of treatment, the relative abundance represented by several phyla, including Bacteroidetes, Firmicutes, and Verrucomicrobia, is considerably smaller in the shotgun data than in the amplicon datasets, while the fraction ascribed to Actinobacteria is larger. Differential representation of these specific taxa within the databases used to ascribe taxonomy to 16S and shotgun datasets could explain these results; however, at the phylum level, representation for two of these lineages (Firmicutes and Bacteroidetes) is actually greater in the database used for shotgun data annotation (Supplementary Fig. 5a ). Similar differences between 16S rRNA and shotgun data have been noted for these and other lineages in recent studies  [24] [25] [26]  , and could be due to differences in representation at the lower taxonomic level, differences in 16S rRNA copy number, or in PCR amplification bias in 16S rRNA datasets.
We also noted that in the co-assembled contig dataset, a larger percentage of reads (approximately 10%) were not assignable to the 13 most abundant phyla observed in the 16S rRNA data; this is likely partially attributable to our ability to detect with shotgun data bacterial abundance from candidate phyla and other taxa not amplifiable by amplicon primers used in this study (Supplementary Fig. 5b ). Collectively, these data demonstrate that while platform-specific differences can be observed, previously reported patterns of compositional shifts under drought are found in both amplicon and shotgun datasets. Drought-induced shifts in functional capacity in the rhizosphere microbiome. While amplicon data can allow for a broad survey of community composition across a large experimental design, shotgun data allow for a direct exploration of community functional capacity.
In this study, we first used the shotgun datasets to identify microbial traits that are more prevalent within drought-stressed samples than control-treated samples. To achieve this, we predicted genes for all co-assembled contigs greater than 1,000 bp in length and assigned functional categories to all genes using the Clusters of Orthologous Groups (COG) categories, which resulted in 203,620 bacterial genes with COG annotations for downstream analysis. Previously published analysis of metatranscriptomic data from eight of these samples at peak of drought within control and drought treatments  6  indicated an enrichment of gene activity belonging to carbohydrate transport and metabolism, secondary metabolite transport and metabolism, and amino acid transport and metabolism, within drought-treated rhizospheres, as compared to control.
Here, the shotgun data also revealed enrichment of two of these categories under drought stress at the peak of drought, specifically carbohydrate transport and metabolism and secondary metabolite transport and metabolism (Fig. 2a ). This suggests that the previously observed enrichment in these two functional activities is driven, at least in part, by a shift in overall functional capacity within the rhizosphere community in favor of these traits, and that on average, drought-enriched bacteria encode for a greater genetic repertoire for exchange or use of carbohydrates and secondary metabolites than those that are drought-depleted (Fig. 2a, c ). Additionally, several other categories appear as enriched at peak of the drought in these co-assembled contig data, though they were not observed as enriched in prior metatranscriptome datasets; additional analyses at earlier and later time points reveal that the strength of enrichment for many of these categories builds as drought progresses, and wanes following rewatering after TP8 (Supplementary Fig.
6 ), mirroring the observed shifts in Actinobacteria across this time frame. Interestingly, these shotgun data also reveal that the bacterial microbiome of bulk soil samples has a weaker but significant enrichment in several of these same categories (Fig. 2b ), which was not observed previously in analysis of the metatranscriptomic data  6. More generally, analysis of drought-induced shifts in functional capacity appears to show more similar patterns between rhizosphere and soil communities than was observed in prior analysis of metatranscriptomic activity, with six of ten significantly enriched categories in rhizospheres or soils appearing as enriched under both (Fig. 2a, b ). This could be driven by the subtle but statistically significant drought enrichment in Actinobacteria reads in soil metagenomic datasets at TP8 (log 2 fold change = 0.
584, p-value = 7. 65 × 10 -6 , Supplementary Fig. 4b, d ), which was absent in previously published soil metatranscriptomics datasets  6. Collectively, these data and prior results demonstrate that while differences exist between metagenomic and metatranscriptomic analyses in this system, several functional categories appear as signatures of both droughtenriched community functional capacity and drought-enriched transcriptional activity in the rhizosphere  6, 9. Assembly of 55 draft metagenome-assembled genomes (MAGs) from rhizosphere and soils. The analysis described above provides insight into community-wide functional capacity and activity within drought-stressed rhizospheres, but does not enable a direct comparison of function between specific droughtenriched and drought-depleted community members.
8%. Taxonomic assignment of these MAGs revealed bins belonging to the major root-associated phyla, including Actinobacteria, Proteobacteria, Acidobacteria, and Gemmatimonadetes, among others (Fig. 3a , Supplementary Fig. 7 ). To determine how well these MAGs represent the droughtinduced community compositional patterns within the broader experimental dataset, we mapped all shotgun read data from our study onto the 55 MAGs. Overall, 4. 8% of reads could be mapped to the MAGs. This level is similar to what has been reported recently in genome-resolved metagenomic studies of other soil communities, 2. 7-22. 4% of the reads from each sample were recruited by the MAGs  27. As was observed for the total coassembled contigs derived from shotgun data (Fig. 1c, d ), an analysis of Actinobacterial relative abundance in the MAG dataset across time and treatment revealed broad similarity to amplicon datasets, including enrichment of Actinobacterial abundance during drought treatment, enrichment during early sorghum Drought enriched Drought depleted Rhizosphere Soil Rhizosphere Soil Bacterial Gene Ontology (GO) Category Fig.
2  Bacterial gene functional capacity enrichment in rhizosphere and soil under drought. Bacteria gene ontology (GO) enrichment analysis for all genes derived from co-assembled contigs showing enrichment (panels one and two) or depletion (panels three and four) under drought for rhizospheres or soils at the peak of drought (TP8). The values on the x-axis indicate the fold enrichment ratio of the relative percentages of genes that are upregulated or downregulated under drought in each category relative to the total relative percentage of genes in the corresponding category within the entire dataset. Categories for which there were fewer than five differentially expressed genes were omitted. Solid circles indicate that the enrichment was significant (p-value of < = 0. 05) in a one-sided hypergeometric test.
Two categories discussed in more detail in the Results are shown in bold. development in control-treated rhizospheres, and a loss of enrichment in drought-treated samples following a rewatering period (Fig. 1e, f ). Additional differences between the MAG datasets and the co-assembled contig analysis were also apparent, for instance, higher mapping to Chloroflexi, but are not unexpected given the incomplete collection of rhizosphere genomes for mapping within our assembled MAGs. Further analysis of the relative abundance of Actinobacterial MAGs across treatments revealed differences in response to drought within the rhizosphere. While the relative abundance of all 15 Actinobacterial genomes increased under drought (Fig. 3b ), only ten of the 15 Actinobacterial MAGs exhibited significant enrichment (average log 2 fold change = 4.
phylogenetic framework exhibited no significant enrichment (average log 2 fold change = 1. 33, p-value > 0. 05, Fig. 3b ). These data suggests that even within the phylum Actinobacteria distinct drought response phenotypes may exist. Based on these results, we hypothesized that a comparative genomics approach across these 15 Actinobacterial MAGs may help identify specific genetic differences that contribute to the strength of the enrichment phenotype. MAG-based comparative genomics identifies differentially abundant functions between enriched and nonenriched Actinobacteria. To test this hypothesis, we performed gene predictions and annotations for all 55 MAGs using the Integrated Microbial Genomes database (IMG)  28. A comparison of functional assignments between enriched (n = 10) and nonenriched (n = 5) Actinobacterial MAGs was then performed with the IMG-ER statistical analysis function using individual COG features.
0 0. 5 Host iron status. Given the evidence that iron-related functionality is correlated with drought enrichment phenotypes in Actinobacteria, we hypothesized that iron homeostasis in the root system may be perturbed by drought. To explore this, we performed an analysis of host transcription using recently published RNA-Seq data  32  that came from the same drought-stressed sorghum root samples used for the microbiome analysis described above. First, we identified within the Phytozome database  33  a subset of 281 sorghum gene features expected to be involved in iron homeostasis based on annotation and homology to rice  34  , maize  35, 36  , and Arabidopsis 37 (Supplementary Data 3). Next, we extracted the log 2 fold change in FPKM (fragments per kilobase of transcript per million mapped reads) values from root tissue samples between control and drought conditions across 15 time points (TP3-TP17), including the six time points (TP3-TP8) during which drought stress was imposed and nine time points (TP9-TP17) that fall within the subsequent rewatering treatment  32.
Discussion Whole-genome metagenomic sequencing provides tremendous value for microbiome studies, allowing for comparisons to the results obtained with more commonly used techniques and also analyses not possible with other data types  43. In this study, metagenome data helped validate broad compositional shifts obtained through amplicon techniques in the same study  6  , corroborating previously described enrichment of Actinobacteria in drought-stressed roots observed in a variety of plant systems  6, [9] [10] [11] 14, 15. Addit onally, these new analyses revealed datatype specific differences, for instance, overrepresentation of Proteobacteria and Actinobacteria, and the reduced representation of phylum Bacteroidetes, in metagenomic datasets. Similar discrepancies between amplicon and shotgun datasets from the same environmental samples have been noted in other recent studies  [24] [25] [26] 44  , and may be ascribed to biases found in amplicon data types, for example, reduced PCR and reference gene copy number-related issues  45, 46  , or biases in shotgun datasets, such as those imposed by incomplete taxonomic representation in reference databases, taxonomic bias in gene annotation, and greater variability in analytical output from available analysis pipelines  43.
Analysis of microbiome composition through multiple methodologies can help identify these potential biases, and may help establish more accurate bounds on compositional shifts than possible through any single technique. Additionally, and more importantly, while amplicon data's significantly reduced cost per sample typically allows analysis of a much larger number of samples, giving increased statistical power for measuring compositional shifts with amplicon datasets  43  (Supplementary Data 4), metagenomic data allow for direct assessment of functional capacity that can only be imprecisely inferred from amplicon datasets. In this study, analysis of shotgun metagenomics data revealed that previously observed shifts in root microbiome activity for carbohydrate and secondary metabolite transport and metabolism during drought are driven at least partially by increased coding capacity for these pathways in drought-enriched lineages.