Search

Microbiome data are subject to experimental bias that is caused by DNA extraction, PCR amplification among other sources, but this important feature is often ignored when developing statistical methods for analyzing microbiome data. McLaren, Willis and Callahan (2019) proposed a model for how such bias affects the observed taxonomic profiles, which assumes main effects of bias without taxon-taxon interactions. Our newly developed method, LOCOM (logistic regression for compositional analysis) for testing differential abundance of taxa, is the first method that accounted for experimental bias and is robust to the main effect biases. However, there is also evidence for taxon-taxon interactions. In this report, we formulated a model for interaction biases and used simulations based on this model to evaluate the impact of interaction biases on the performance of LOCOM as well as other available compositional analysis methods. Our simulation results indicated that LOCOM remained robust to a reasonable range of interaction biases. The other methods tended to have inflated FDR even when there were only main effect biases. LOCOM maintained the highest sensitivity even when the other methods cannot control the FDR. We thus conclude that LOCOM outperforms the other methods for compositional analysis of microbiome data considered here.

Recent advancements of multimodal neuroimaging such as functional MRI (fMRI) and diffusion MRI (dMRI) offers unprecedented opportunities to understand brain development. Most existing neurodevelopmental studies focus on using a single imaging modality to study microstructure or neural activations in localized brain regions. The developmental changes of brain network architecture in childhood and adolescence are not well understood. Our study made use of dMRI and resting-state fMRI imaging data sets from Philadelphia Neurodevelopmental Cohort (PNC) study to characterize developmental changes in both structural as well as functional brain connectomes. A multimodal multilevel model (MMM) is developed and implemented in PNC study to investigate brain maturation in both white matter structural connection and intrinsic functional connection. MMM addresses several major challenges in multimodal connectivity analysis. First, by using a first-level data generative model for observed measures and a second-level latent network modeling, MMM effectively infers underlying connection states from noisy imaging-based connectivity measurements. Secondly, MMM models the interplay between the structural and functional connections to capture the relationship between different brain connectomes. Thirdly, MMM incorporates covariate effects in the network modeling to investigate network heterogeneity across subpopoulations. Finally, by using a module-wise parameterization based on brain network topology, MMM is scalable to whole-brain connectomics. MMM analysis of the PNC study generates new insights in neurodevelopment during adolescence including revealing the majority of the white fiber connectivity growth are related to the cognitive networks where the most significant increase is found between the default mode and the executive control network with a 15% increase in the probability of structural connections. We also uncover functional connectome development mainly derived from global functional integration rather than direct anatomical connections. To the best of our knowledge, these findings have not been reported in the literature using multimodal connectomics. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

BACKGROUND: Evidence suggests that intravaginal practices (IVPs) women use to cleanse their vagina or enhance sexual pleasure may be associated with unhealthy changes in the vaginal microbiome (VM). However, the effects of these practices in postmenopausal women are unknown. OBJECTIVES: The objective of this pilot study was to characterize the VM communities of postmenopausal women, identify types and frequency of IVPs, and explore associations between the VM and IVPs in postmenopausal women. METHODS: We analyzed the VM data of 21 postmenopausal women in Atlanta, Georgia, from vaginal swabs collected at a routine gynecological visit. 16S rRNA gene sequencing in the V3–V4 region was used to characterize the VM. In Addition, we described the intravaginal practices of these women, identified by using our newly developed instrument: the Vaginal Cleansing Practices questionnaire. The associations between the VM and intravaginal practices were explored by comparing the alpha diversities, beta diversities, and the relative abundances at both the community level and individual genus level. RESULTS: The most abundant known bacterial genus found in the VM samples was Lactobacillus (35.7%), followed by Prevotella (21.4%). Eleven women (52%) reported using at least one type of intravaginal practice since menopause. The most common type of intravaginal practice was soap and water to clean inside the vagina. The use of intravaginal practices was not associated with any alpha diversity metric, including Shannon Index, Inverse Simpson Index, and Chao1 Index; beta diversity metric, including Bray-Curtis and Jaccard distances; nor relative abundances at the community and individual genus level. Sociodemographic factors were also not to be associated with any alpha diversity metric. DISCUSSION: Clinicians must assess intravaginal practices and other vaginal and sexual hygiene practices of women of all ages to educate and promote healthy behaviors. More than half of the postmenopausal women in this pilot study use intravaginal practices. Understanding the reasoning behind participants’ use of intravaginal practices and their perceptions of the possible effects of these practices will require further research. While the small sample did not show associations with the VM, more extensive studies are warranted.

In recent years, ultraviolet–visible spectroscopy has been widely used for chemical oxygen demand (COD) measurements of water. However, chemical compositions of substance in different water samples can cause measurement deviations, so a local calibration is needed. In this study, a novel local calibration method is proposed. The absorption spectra of COD standard solutions and wastewater samples taken from four factories were collected. We analyzed the impact of chemical compositions of substance in different water samples and extracted the morphology features of their absorptive spectra for recognition models. Furthermore, we calculated the local calibration parameters of the four categories of real water samples by specific modification based on the ability of light absorption in various water environments. After the process of local calibration, the root mean square errors (RMSEs) of the predictions were very small, which highlights the potential of this method for improving the accuracy and adaptability of COD measurements based on ultraviolet–visible spectrum.

This paper describes a new turbidity transducer based on color measurement. The absorbance of solutions reflects the absorption and scattering of suspended particle for incident light which could determine the turbidity of solutions. The experimental results indicate that there are good linear relationships between chromaticity and turbidity. The new way is suitable for continuous monitoring of water turbidity in the wide range.