Additional file 2: Figure S1. of Gastrointestinal microbial populations can distinguish pediatric and adolescent Acute Lymphoblastic Leukemia (ALL) at the time of disease diagnosis

Box-plots of the alpha diversity of OTUs at 95 % and 90 % identity threshold of the Control and Patient groups. The Patient group is further partitioned into the group taking antibiotics 1-month period Visit 1 (Patient_A) and the group not taking antibiotics (Patient_NA). (A) Alpha diversity for 95 % OTUs, the Y-axis denotes alpha diversity (Shannon Index values). The Wilcoxon Rank Sum test p-value = 0.00253 for Control vs Patient, p-value = 0.00328 for Control vs Patient_A and p-value = 0.05969 for Control vs Patient_NA. (B) Alpha diversity for 90 % OTUs, the Y-axis denotes alpha diversity (Shannon Index values). The Wilcoxon Rank Sum test p-value = 0.00156 for Control vs Patient, p-value = 0.00328 for Control vs Patient_A and p-value = 0.03119 for Control vs Patient_NA. In both cases (A, B) the Patient group has a lower microbiota diversity (statistically significant) compared to the Control group (p-value < 0.0026). The diversities of the Patient_A and Patient_NA groups are also significantly lower (p-value < 0.05) than the Control group. (PDF 203 kb

A phylogenetic tree built from amino acid sequences using FastTree for the gene encoding the large subunit terminase.

<p>Selected reference sequences are colored red and green. Representative Indian Ocean VF and LF sequences are colored pink and purple respectively. GOS Phase I sequences are colored blue. Confidence values are displayed on the tree.</p

Functional characterization of Indian Ocean viral sequences from the viral and larger fractions of metagenomic data in the context of KEGG pathways.

<p>The inset pie charts represent the breakdown of the Metabolism super-pathway (top) and Energy metabolism pathway (bottom). The percentages of viral sequences attributed to the Energy metabolism pathway are indicated on the Metabolism pie charts.</p

Sanger and 454 pyrosequencing statistics for Indian Ocean viral metagenomes.

*<p>Sample not used in taxonomic and functional analyses due to cellular contamination. n/a denote where the data was not used in study. Percent of phylogenetic trees denotes the amount of sequences (ORFs) that were taxonomically classified via APIS.</p

Taxonomic characterization of Indian Ocean virioplankton assemblages.

a<p>% total protein sequences classified at highest (kingdom) taxonomic level per category (LF or VF).</p>b<p>% total protein sequences characterized as viral per category.</p>c<p>% of total of protein sequences characterized as dsDNA viruses comprising >0.1% in one category.</p>d<p>% of total protein sequences characterized as Caudoviruses per category.</p>e<p>% of total protein sequences characterized as NCLDVs per category.</p>f<p>% of total protein sequences characterized as Phycoviruses per category.</p><p>n/a = not available.</p

Characterization of circulating RSV strains among subjects in the OUTSMART-RSV surveillance program during the 2016-17 winter viral season in the United States

<div><p>Background</p><p>Respiratory syncytial virus (RSV) is an established cause of serious lower respiratory disease in infants, elderly and high-risk populations. The OUTSMART surveillance program aims to characterize patient populations and currently circulating RSV strains, and monitor temporal and geographic evolution of RSV F and G proteins in the U.S.</p><p>Methods</p><p>The OUTSMART 2016–17 study collected RSV-positive samples from 25 RSVAlert^® laboratories from 4 U.S. regions and Puerto Rico during November 2016 through March 2017. Frequencies of A and B subtypes and genotypes were determined for several demographic and geographic variables. To gauge the representativeness of the OUTSMART patients, results were compared to discharge data from the NEDS and NIS databases.</p><p>Results</p><p>A total of 1,041 RSV-positive samples with associated demographic data were obtained and the RSV F gene and second variable region of the G gene were sequenced. The majority of samples (76.0%) came from children under 2 years old: <1 year (48.4%), 1–2 years (27.6%). The OUTSMART patient sample was similar to NEDS and NIS for age, gender, and geographic location. Both OUTSMART and national RSV cases peaked in January. Of OUTSMART samples, 45.3% were subtype A, 53.7% were subtype B and 1.0% were mixed A and B. The percentage of RSV B cases increased with increasing age. Hospitalization (length of hospital stay, LOS, >24 hrs) occurred in 29.0% of patients of which 52.0% had RSV B. Outpatients (LOS <24 hrs) were 64.4% of total of which 73.3% were diagnosed in the ER and discharged, while only 6% were diagnosed in other outpatient settings.</p><p>Conclusions</p><p>The OUTSMART 2016–17 study was representative of the U.S. RSV experience. Geographic and temporal information from the RSV surveillance program will be used to establish a molecular baseline of RSV F and G sequence variability and to help inform development of novel agents for RSV prophylaxis and treatment.</p></div

A model of RsfA action.

<p>In rich medium and during exponential growth, RsfA is either not present or not active, so that protein synthesis is fully active. In starving cells, RsfA binds to ribosomal L14 and, as a consequence, blocks ribosomal subunit joining and thus protein synthesis.</p