Zentrale Stelle der Landesjustizverwaltungen zur Aufklärung nationalsozialistischer Verbrechen (ZStL) T.V., E.A.F. and R.S. analysed the metagenomic sequencing data. C.J.S., N.J.A. and J.F.P. generated the metagenomic sequencing data. S.T., T.D.A. and H.V. designed and conducted bacterial growth assays. K.V., Å.L., W.A.H., M.J.R., J.-X.S., J.T., A.-G.Z., B.A. and J.P.K. contributed to the study concept, design and sample acquisition. H.L., H.V., C.H. and R.J.X. served as principal investigators. T.V., E.A.F., H.V., C.H. and R.J.X drafted the manuscript. All authors discussed the results, contributed to critical revisions and approved the final manuscript. Members of the TEDDY Study Group are listed in the Supplementary Information. Corresponding authors Custom Bunny Stuffed-Flower Girl Bunny Gift-Bunny Plush-Rabbit Plushies-Stuffed Animal Toy-Gift For Baby Kid Toddler-Baby Shower Gift

Markle, J. G. et al. Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 339, 1084–1088 (2013). The HMO gene cluster homologues between B. longum subsp. infantis and multiple taxa were analysed as follows. UniRef90 gene families corresponding to the protein sequences in the B. longum subsp. infantis HMO gene cluster 39 (protein sequences Blon_2331-Blon_2361 in NCBI protein sequence database) were identified by translated BLAST search against ChocoPhlAn pangenome collection 48 used by HUMAnN2. Identified hits were further filtered by requiring ≥50% alignment identity and ≥80% mutual coverage. Combining this information with HUMAnN2 species-stratified UniRef90 gene family quantification enabled calling these genes present given that they had sufficient read coverage, here defined as log 10(counts per million) > 0.1 in at least 50 samples collected during breastfeeding. Differential gene prevalence during breastfeeding was tested using the samples in which the carrier species had >1% relative abundance. Testing was conducted using the test of equal or given proportions (prop.test function in R) and by comparing the prevalence (proportion of the samples for which the species in question harboured the gene according to the metagenomic data) of the gene in samples collected during breastfeeding with the samples collected after weaning. P values were adjusted for multiple testing by Benjamini–Hochberg method (p.adjust function in R). All homologues together with their BLAST search metrics, prevalence in the metagenomic data and corresponding B. infantis HMO gene are reported in Supplementary Table 5. Bacterial growth assays

Hagopian, W. A. et al. The Environmental Determinants of Diabetes in the Young (TEDDY): genetic criteria and international diabetes risk screening of 421 000 infants. Pediatr. Diabetes 12, 733–743 (2011). Barker, J. M. et al. Clinical characteristics of children diagnosed with type 1 diabetes through intensive screening and follow-up. Diabetes Care 27, 1399–1404 (2004). A priori power calculations using discrete Cox’s proportional hazards regression 44 for the matched IA case–control study estimated 80% power, α = 0.01, two-sided test to detect an odds ratio > 3 for an exposure with 5% prevalence, to an odds ratio > 1.8 for an exposure with 20% prevalence. The experiments were not randomized, and investigators were not blinded to allocation during experiments and outcome assessment. Metagenomic sequencing and initial bioinformatics Consortium-based approach to receiving an EMA qualification opinion on the use of islet autoantibodies as enrichment biomarkers in type 1 diabetes clinical studies

Keep collections to yourself or inspire other shoppers! Keep in mind that anyone can view public collections - they may also appear in recommendations and other places. Lee, H. S. et al. Biomarker discovery study design for type 1 diabetes in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Diabetes Metab. Res. Rev. 30, 424–434 (2014). FAST Turnaround, 15" Memory Bear, Custom Teddy Bear from Loved Ones Clothes, Memorial Bear, Keepsake Stuffed Bear, Personalized RemembranceHealth Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, FL, USA Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, Turku, Finland