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  • 1
    Online Resource
    Online Resource
    Frontiers Media SA ; 2023
    In:  Frontiers in Microbiology Vol. 14 ( 2023-4-17)
    In: Frontiers in Microbiology, Frontiers Media SA, Vol. 14 ( 2023-4-17)
    Abstract: Microbial communities inhabiting the human infant gut are important for immune system development and lifelong health. One critical exposure affecting the bacterial colonization of the infant gut is consumption of human milk, which contains diverse microbial communities and prebiotics. We hypothesized that human milk-associated microbial profiles are associated with those of the infant gut. Methods Maternal–infant dyads enrolled in the New Hampshire Birth Cohort Study ( n  = 189 dyads) contributed breast milk and infant stool samples collected approximately at 6 weeks, 4 months, 6 months, 9 months, and 12 months postpartum ( n  = 572 samples). Microbial DNA was extracted from milk and stool and the V4-V5 region of the bacterial 16S rRNA gene was sequenced. Results Clustering analysis identified three breast milk microbiome types (BMTs), characterized by differences in Streptococcus , Staphylococcus , Pseudomonas , Acinetobacter , and microbial diversity. Four 6-week infant gut microbiome types (6wIGMTs) were identified, differing in abundances of Bifidobacterium , Bacteroides , Clostridium , Streptococcus , and Escherichia / Shigella , while two 12-month IGMTs (12mIGMTs) differed primarily by Bacteroides presence. At 6 weeks, BMT was associated with 6wIGMT (Fisher’s exact test value of p  = 0.039); this association was strongest among infants delivered by Cesarean section (Fisher’s exact test value of p  = 0.0028). The strongest correlations between overall breast milk and infant stool microbial community structures were observed when comparing breast milk samples to infant stool samples collected at a subsequent time point, e.g., the 6-week breast milk microbiome associated with the 6-month infant gut microbiome (Mantel test Z -statistic = 0.53, value of p  = 0.001). Streptoccous and Veillonella species abundance were correlated in 6-week milk and infant stool, and 4- and 6-month milk Pantoea species were associated with infant stool Lachnospiraceae genera at 9 and 12  months. Discussion We identified clusters of human milk and infant stool microbial communities that were associated in maternal–infant dyads at 6 weeks of life and found that milk microbial communities were more strongly associated with infant gut microbial communities in infants delivered operatively and after a lag period. These results suggest that milk microbial communities have a long-term effect on the infant gut microbiome both through sharing of microbes and other molecular mechanisms.
    Type of Medium: Online Resource
    ISSN: 1664-302X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2587354-4
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  • 2
    Online Resource
    Online Resource
    Frontiers Media SA ; 2019
    In:  Frontiers in Microbiology Vol. 10 ( 2019-12-20)
    In: Frontiers in Microbiology, Frontiers Media SA, Vol. 10 ( 2019-12-20)
    Type of Medium: Online Resource
    ISSN: 1664-302X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2019
    detail.hit.zdb_id: 2587354-4
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  • 3
    In: Frontiers in Microbiology, Frontiers Media SA, Vol. 12 ( 2021-4-7)
    Abstract: Cesarean-delivered (CD) infants harbor a distinct gut microbiome from vaginally delivered (VD) infants, however, during infancy, the most important driver of infant gut microbial colonization is infant feeding. Earlier studies have shown that breastfeeding is associated with higher levels of health-promoting bacteria such and Bifidobacterium and Bacteroides via modulation of the immune system, and production of metabolites. As the infant gut matures and solid foods are introduced, it is unclear whether longer duration of breast feeding restore loss of beneficial taxa within the intestinal microbiota of operatively delivered infants. Within the New Hampshire Birth Cohort Study, we evaluated the longitudinal effect of delivery mode and infant feeding on the taxonomic composition and functional capacity of developing gut microbiota in the First year of life. Microbiota of 500 stool samples collected between 6 weeks and 12 months of age (from 229 infants) were characterized by 16S ribosomal RNA sequencing. Shotgun metagenomic sequencing was also performed on 350 samples collected at either 6 weeks or 12 months of age. Among infant participants, 28% were cesarean-delivered (CD) infants and most (95%) initiated breastfeeding within the first six months of life, with 26% exclusively breastfed and 69% mixed-fed (breast milk and formula), in addition to complementary foods by age 1. Alpha (within-sample) diversity was significantly lower in CD infants compared to vaginally delivered (VD) infants ( P & lt; 0.05) throughout the study period. Bacterial community composition clustering by both delivery mode and feeding duration at 1 year of age revealed that CD infants who were breast fed for & lt; 6 months were more dissimilar to VD infants than CD infants who breast fed for ≥ 6 months. We observed that breastfeeding modified the longitudinal impact of delivery mode on the taxonomic composition of the microbiota by 1 year of age, with an observed increase in abundance of Bacteroides fragilis and Lactobacillus with longer duration of breastfeeding among CD infants while there was an increase in Faecalibacterium for VD infants. Our findings confirm that duration of breastfeeding plays a critical role in restoring a health-promoting microbiome, call for further investigations regarding the association between breast milk exposure and health outcomes in early life.
    Type of Medium: Online Resource
    ISSN: 1664-302X
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2587354-4
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  • 4
    In: Frontiers in Microbiomes, Frontiers Media SA, Vol. 3 ( 2024-6-3)
    Abstract: The microbial interactions within the human microbiome are complex, and few methods are available to identify these interactions within a longitudinal microbial abundance framework. Existing methods typically impose restrictive constraints, such as requiring long sequences and equal spacing, on the data format which in many cases are violated. Methods To identify microbial interaction networks (MINs) with general longitudinal data settings, we propose a stationary Gaussian graphical model (SGGM) based on 16S rRNA gene sequencing data. In the SGGM, data can be arbitrarily spaced, and there are no restrictions on the length of data sequences from a single subject. Based on the SGGM, EM -type algorithms are devised to compute the L 1-penalized maximum likelihood estimate of MINs. The algorithms employ the classical graphical LASSO algorithm as the building block and can be implemented efficiently. Results Extensive simulation studies show that the proposed algorithms can significantly outperform the conventional algorithms if the correlations among the longitudinal data are reasonably high. When the assumptions in the SGGM areviolated, e.g., zero inflation or data from heterogeneous microbial communities, the proposed algorithms still demonstrate robustness and perform better than the other existing algorithms. The algorithms are applied to a 16S rRNA gene sequencing data set from patients with cystic fibrosis. The results demonstrate strong evidence of an association between the MINs and the phylogenetic tree, indicating that the genetically related taxa tend to have more/stronger interactions. These results strengthen the existing findings in literature. Discussion The proposed algorithms can potentially be used to explore the network structure in genome, metabolome etc. as well.
    Type of Medium: Online Resource
    ISSN: 2813-4338
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2024
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  • 5
    Online Resource
    Online Resource
    Frontiers Media SA ; 2021
    In:  Frontiers in Public Health Vol. 9 ( 2021-11-5)
    In: Frontiers in Public Health, Frontiers Media SA, Vol. 9 ( 2021-11-5)
    Abstract: What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks? Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images. Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors. Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age. Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.
    Type of Medium: Online Resource
    ISSN: 2296-2565
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2711781-9
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  • 6
    Online Resource
    Online Resource
    Institute of Mathematical Statistics ; 2021
    In:  The Annals of Applied Statistics Vol. 15, No. 4 ( 2021-12-1)
    In: The Annals of Applied Statistics, Institute of Mathematical Statistics, Vol. 15, No. 4 ( 2021-12-1)
    Type of Medium: Online Resource
    ISSN: 1932-6157
    Language: Unknown
    Publisher: Institute of Mathematical Statistics
    Publication Date: 2021
    detail.hit.zdb_id: 2376910-5
    detail.hit.zdb_id: 2253657-7
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