Recent studies have suggested that the intestinal microbiome plays an important role in modulating risk of several chronic diseases, including inflammatory bowel disease, obesity, type 2 diabetes, ...cardiovascular disease, and cancer. At the same time, it is now understood that diet plays a significant role in shaping the microbiome, with experiments showing that dietary alterations can induce large, temporary microbial shifts within 24 h. Given this association, there may be significant therapeutic utility in altering microbial composition through diet. This review systematically evaluates current data regarding the effects of several common dietary components on intestinal microbiota. We show that consumption of particular types of food produces predictable shifts in existing host bacterial genera. Furthermore, the identity of these bacteria affects host immune and metabolic parameters, with broad implications for human health. Familiarity with these associations will be of tremendous use to the practitioner as well as the patient.
This paper develops a nonparametric inference framework that is applicable to occupation time curves derived from wearable device data. These curves consider all activity levels within the range of ...device readings, which is preferable to the practice of classifying activity into discrete categories. Motivated by certain features of these curves, we introduce a powerful likelihood ratio approach to construct confidence bands and compare functional means. Notably, our approach allows discontinuities in the functional covariances while accommodating discretization of the observed trajectories. A simulation study shows that the proposed procedures outperform competing functional data procedures. We illustrate the proposed methods using wearable device data from an NHANES study.
Psoriasis impacts 1-3% of the world's population and is characterized by hyper-proliferation of keratinocytes and increased inflammation. At the molecular level, psoriasis is commonly driven by a ...Th17 response, which serves as a major therapeutic target. Microbiome perturbations have been associated with several immune-mediated diseases such as atopic dermatitis, asthma, and multiple sclerosis. Although a few studies have investigated the association between the skin microbiome and psoriasis, conflicting results have been reported plausibly due to the lack of standardized sampling and profiling protocols, or to inherent microbial variability across human subjects and underpowered studies. To better understand the link between the cutaneous microbiota and psoriasis, we conducted an analysis of skin bacterial communities of 28 psoriasis patients and 26 healthy subjects, sampled at six body sites using a standardized protocol and higher sequencing depth compared to previous studies. Mouse studies were employed to examine dermal microbial-immune interactions of bacterial species identified from our study.
Skin microbiome profiling based on sequencing the 16S rRNA V1-V3 variable region revealed significant differences between the psoriasis-associated and healthy skin microbiota. Comparing the overall community structures, psoriasis-associated microbiota displayed higher diversity and more heterogeneity compared to healthy skin bacterial communities. Specific microbial signatures were associated with psoriatic lesional, psoriatic non-lesional, and healthy skin. Specifically, relative enrichment of Staphylococcus aureus was strongly associated with both lesional and non-lesional psoriatic skin. In contrast, Staphylococcus epidermidis and Propionibacterium acnes were underrepresented in psoriatic lesions compared to healthy skin, especially on the arm, gluteal fold, and trunk. Employing a mouse model to further study the impact of cutaneous Staphylcoccus species on the skin T cell differentiation, we found that newborn mice colonized with Staphylococcus aureus demonstrated strong Th17 polarization, whereas mice colonized with Staphylococcus epidermidis or un-colonized controls showed no such response.
Our results suggest that microbial communities on psoriatic skin is substantially different from those on healthy skin. The psoriatic skin microbiome has increased diversity and reduced stability compared to the healthy skin microbiome. The loss of community stability and decrease in immunoregulatory bacteria such as Staphylococcus epidermidis and Propionibacterium acnes may lead to higher colonization with pathogens such as Staphylococcus aureus, which could exacerbate cutaneous inflammation along the Th17 axis.
Psoriasis is an inflammatory, IL-17–driven skin disease in which autoantigen-induced CD8+ T cells have been identified as pathogenic drivers.
Our study focused on comprehensively characterizing the ...phenotypic variation of CD8+ T cells in psoriatic lesions.
We used single-cell RNA sequencing to compare CD8+ T-cell transcriptomic heterogeneity between psoriatic and healthy skin.
We identified 11 transcriptionally diverse CD8+ T-cell subsets in psoriatic and healthy skin. Among several inflammatory subsets enriched in psoriatic skin, we observed 2 Tc17 cell subsets that were metabolically divergent, were developmentally related, and expressed CXCL13, which we found to be a biomarker of psoriasis severity and which achieved comparable or greater accuracy than IL17A in a support vector machine classifier of psoriasis and healthy transcriptomes. Despite high coinhibitory receptor expression in the Tc17 cell clusters, a comparison of these cells with melanoma-infiltrating CD8+ T cells revealed upregulated cytokine, cytolytic, and metabolic transcriptional activity in the psoriatic cells that differed from an exhaustion program.
Using high-resolution single-cell profiling in tissue, we have uncovered the diverse landscape of CD8+ T cells in psoriatic and healthy skin, including 2 nonexhausted Tc17 cell subsets associated with disease severity.
Polycyclic aromatic hydrocarbons are widespread urban air pollutants from combustion of fossil fuel and other organic material shown previously to be neurotoxic.
In a prospective cohort study, we ...evaluated the relationship between Attention Deficit Hyperactivity Disorder behavior problems and prenatal polycyclic aromatic hydrocarbon exposure, adjusting for postnatal exposure.
Children of nonsmoking African-American and Dominican women in New York City were followed from in utero to 9 years. Prenatal polycyclic aromatic hydrocarbon exposure was estimated by levels of polycyclic aromatic hydrocarbon- DNA adducts in maternal and cord blood collected at delivery. Postnatal exposure was estimated by the concentration of urinary polycyclic aromatic hydrocarbon metabolites at ages 3 or 5. Attention Deficit Hyperactivity Disorder behavior problems were assessed using the Child Behavior Checklist and the Conners Parent Rating Scale- Revised.
High prenatal adduct exposure, measured by elevated maternal adducts was significantly associated with all Conners Parent Rating Scale-Revised subscales when the raw scores were analyzed continuously (N = 233). After dichotomizing at the threshold for moderately to markedly atypical symptoms, high maternal adducts were significantly associated with the Conners Parent Rating Scale-Revised DSM-IV Inattentive (OR = 5.06, 95% CI 1.43, 17.93) and DSM-IV Total (OR = 3.37, 95% CI 1.10, 10.34) subscales. High maternal adducts were positivity associated with the DSM-oriented Attention Deficit/Hyperactivity Problems scale on the Child Behavior Checklist, albeit not significant. In the smaller sample with cord adducts, the associations between outcomes and high cord adduct exposure were not statistically significant (N = 162).
The results suggest that exposure to polycyclic aromatic hydrocarbons encountered in New York City air may play a role in childhood Attention Deficit Hyperactivity Disorder behavior problems.
Among the different Science, Technology, Engineering, and Math fields, engineering continues to have one of the highest rates of attrition (Hewlett et al., 2008). The turnover rate for women ...engineers from engineering fields is even higher than for men (Frehill, 2010). Despite increased efforts from researchers, there are still large gaps in our understanding of the reasons that women leave engineering. This study aims to address this gap by examining the reasons
women leave engineering. Specifically, we analyze the reasons for departure given by national sample of 1,464 women engineers who left the profession after having worked in the engineering field. We applied a person-environment fit theoretical lens, in particular, the Theory of Work Adjustment (TWA) (Dawis and Lofquist, 1984) to understand and categorize the reasons for leaving the engineering field. According to the TWA, occupations have different "reinforcer patterns," reflected in six occupational values, and a mismatch between the reinforcers provided by the work environment and individuals' needs may trigger departure from the environment. Given the paucity of literature in this area, we posed research questions to explore the reinforcer pattern of values implicated in women's decisions to leave the engineering field. We used qualitative analyses to understand, categorize, and code the 1,863 statements that offered a glimpse into the myriad reasons that women offered in describing their decisions to leave the engineering profession. Our results revealed the top three sets of reasons underlying women's decision to leave the jobs and engineering field were related to: first, poor and/or inequitable compensation, poor working conditions, inflexible and demanding work environment that made work-family balance difficult; second, unmet achievement needs that reflected a dissatisfaction with effective utilization of their math and science skills, and third, unmet needs with regard to lack of recognition at work and adequate opportunities for advancement. Implications of these results for future research as well as the design of effective intervention programs aimed at women engineers' retention and engagement in engineering are discussed.
This article compares distribution functions among pairs of locations in their domains, in contrast to the typical approach of univariate comparison across individual locations. This bivariate ...approach is studied in the presence of sampling bias, which has been gaining attention in COVID-19 studies that over-represent more symptomatic people. In cases with either known or unknown sampling bias, we introduce Anderson-Darling-type tests based on both the univariate and bivariate formulation. A simulation study shows the superior performance of the bivariate approach over the univariate one. We illustrate the proposed methods using real data on the distribution of the number of symptoms suggestive of COVID-19.
In this study, a new type of field-effect transistor (FET)-based biosensor is demonstrated to be able to overcome the problem of severe charge-screening effect caused by high ionic strength in ...solution and detect proteins in physiological environment. Antibody or aptamer-immobilized AlGaN/GaN high electron mobility transistors (HEMTs) are used to directly detect proteins, including HIV-1 RT, CEA, NT-proBNP and CRP, in 1X PBS (with 1%BSA) or human sera. The samples do not need any dilution or washing process to reduce the ionic strength. The sensor shows high sensitivity and the detection takes only 5 minutes. The designs of the sensor, the methodology of the measurement, and the working mechanism of the sensor are discussed and investigated. A theoretical model is proposed based on the finding of the experiments. This sensor is promising for point-of-care, home healthcare, and mobile diagnostic device.
In data analysis using dimension reduction methods, the main goal is to summarize how the response is related to the covariates through a few linear combinations. One key issue is to determine the ...number of independent, relevant covariate combinations, which is the dimension of the sufficient dimension reduction (SDR) subspace. In this work, we propose an easily-applied approach to conduct inference for the dimension of the SDR subspace, based on augmentation of the covariate set with simulated pseudo-covariates. Applying the partitioning principal to the possible dimensions, we use rigorous sequential testing to select the dimensionality, by comparing the strength of the signal arising from the actual covariates to that appearing to arise from the pseudo-covariates. We show that under a "uniform direction" condition, our approach can be used in conjunction with several popular SDR methods, including sliced inverse regression. In these settings, the test statistic asymptotically follows a beta distribution and therefore is easily calibrated. Moreover, the family-wise type I error rate of our sequential testing is rigorously controlled. Simulation studies and an analysis of newborn anthropometric data demonstrate the robustness of the proposed approach, and indicate that the power is comparable to or greater than the alternatives.
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