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Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for the diagnosis of some chronic disorders. Specifically, pre-treatment EEG signals in the alpha and theta frequency bands have demonstrated some association with antidepressant response, which is well-known to have a low response rate. We aim to design an integrated pipeline that improves the response rate of patients with major depressive disorder by developing a treatment policy guided by the resting state pre-treatment EEG recordings and other treatment effects modifiers. First, we design an innovative automatic site-specific EEG preprocessing pipeline to extract features with stronger signals than raw data. We then estimate the conditional average treatment effect (CATE) using causal forests and use a doubly robust technique to improve efficiency in the estimation of the average treatment effect. We present evidence of heterogeneity in the treatment effect and the modifying power of the EEG features, as well as a significant average treatment effect, a result that cannot be obtained with conventional methods. Finally, we employ an efficient policy learning algorithm to learn an optimal depth-2 treatment assignment decision tree and compare its performance with Q-Learning and outcome-weighted learning via simulation studies and an application to a large multi-site, double-blind, randomized controlled clinical trial, EMBARC.

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Modern neuroimaging technologies have substantially advanced the measurement of brain activity. Electroencephalogram (EEG) as a noninvasive neuroimaging technique measures changes in electrical voltage on the scalp induced by brain cortical activity. With its high temporal resolution, EEG has emerged as an increasingly useful tool to study brain connectivity. Challenges with modeling EEG signals of complex brain activity include interactions among unknown sources, low signal-to-noise ratio, and substantial between-subject heterogeneity. In this work, we propose a state space model that jointly analyzes multichannel EEG signals and learns dynamics of different sources corresponding to brain cortical activity. Our model borrows strength from spatially correlated measurements and uses low-dimensional latent states to explain all observed channels. The model can account for patient heterogeneity and quantify the effect of a subject's covariates on the latent space. The EM algorithm, Kalman filtering, and bootstrap resampling are used to fit the state space model and provide comparisons between patient diagnostic groups. We apply the developed approach to a case-control study of alcoholism and reveal significant attenuation of brain activity in response to visual stimuli in alcoholic subjects compared to healthy controls.

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Falls in the aging population are a major public health concern. Outdoor falls in community-dwelling older adults are often triggered by uneven pedestrian walkways. Our understanding of the motor control adaptations to walk over an uneven surface, and the effects of aging on these adaptations is sparse. Here, we study changes in muscle co-contraction, a clinically accepted measure of motor control, due to changes in walking surfaces typically encountered in the outdoor built environment. We address the following research questions: 1) are there walking surface and sex-based differences in muscle co-contractions between young and older adults? and 2) is muscle co-contraction associated with age? We calculated muscle co-contractions from 13 young and 17 older adults during walking at self-selected speeds over even and uneven brick walkways. Muscle co-contraction at the ankle joint was determined from the tibialis anterior and lateral gastrocnemius muscle pair, and at the knee joint from the rectus femoris and semitendinosus muscle pair. Older adults displayed 8-13% greater ankle muscle co-contractions during walking over uneven compared to even surfaces. We found 55-61% (entire gait) and 73-75% (stance phase) greater ankle muscle co-contractions in older females compared to older males during walking over even and uneven surfaces. We found 31-43% greater knee muscle co-contractions in older females compared to older males during the swing phase of walking over even and uneven surfaces. This study underscores the need for determining muscle co-contractions from even and uneven surfaces for quantifying motor control deficits due to aging or neuromuscular disorders.

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BACKGROUND: Mixed models are a useful tool for evaluating the association between an outcome variable and genetic variables from a family-based genetic study, taking into account the kinship coefficients. When there are ultrahigh dimensional genetic variables (ie, p ≫ n), it is challenging to fit any mixed effect model. METHODS: We propose a two-stage strategy, screening genetic variables in the first stage and then fitting the mixed effect model in the second stage to those variables that survive the screening. For the screening stage, we can use the sure independence screening (SIS) procedure, which fits the mixed effect model to one genetic variable at a time. Because the SIS procedure may fail to identify those marginally unimportant but jointly important genetic variables, we propose a joint screening (JS) procedure that screens all the genetic variables simultaneously. We evaluate the performance of the proposed JS procedure via a simulation study and an application to the GAW20 data. RESULTS: We perform the proposed JS procedure on the GAW20 representative simulated data set (n = 680 participant(s) and p = 463,995 CpG cytosine-phosphate-guanine [CpG] sites) and select the top d = ⌊n/ log(n)⌋ variables. Then we fit the mixed model using these top variables. Under significance level, 5%, 43 CpG sites are found to be significant. Some diagnostic analyses based on the residuals show the fitted mixed model is appropriate. CONCLUSIONS: Although the GAW20 data set is ultrahigh dimensional and family-based having within group variances, we were successful in performing subset selection using a two-step strategy that is computationally simple and easy to understand.

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BACKGROUND: Trovafloxacin is a broad-spectrum antibiotic, recently identified as an inhibitor of pannexin-1 (Panx1) channels. Panx1 channels are important conduits for the adenosine triphosphate (ATP) release from live and dying cells that enhances the inflammatory response of immune cells. Elevated extracellular levels ATP released upon injury activate purinergic pathways in inflammatory cells that promote migration, proliferation, phagocytosis, and apoptotic signals. Here, we tested whether trovafloxacin administration attenuates the neuroinflammatory response and improves outcomes after brain trauma. METHODS: The murine controlled cortical impact (CCI) model was used to determine whether in vivo delivery of trovafloxacin has anti-inflammatory and neuroprotective actions after brain trauma. Locomotor deficit was assessed using the rotarod test. Levels of tissue damage markers and inflammation were measured using western blot, qPCR, and immunofluorescence. In vitro assays were used to evaluate whether trovafloxacin blocks ATP release and cell migration in a chemotactic-stimulated microglia cell line. RESULTS: Trovafloxacin treatment of CCI-injured mice significantly reduced tissue damage markers and improved locomotor deficits. In addition, trovafloxacin treatment significantly reduced mRNA levels of several pro-inflammatory cytokines (IL-1ß, IL-6, and TNF-α), which correlates with an overall reduction in the accumulation of inflammatory cell types (neutrophils, microglia/macrophages, and astroglia) at the injury zone. To determine whether trovafloxacin exerted these effects by direct action on immune cells, we evaluated its effect on ATP release and cell migration using a chemotactic-stimulated microglial cell line. We found that trovafloxacin significantly inhibited both ATP release and migration of these cells. CONCLUSION: Our results show that trovafloxacin administration has pronounced anti-inflammatory and neuroprotective effects following brain injury. These findings lay the foundation for future studies to directly test a role for Panx1 channels in pathological inflammation following brain trauma.

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The use of Bisphenol A (BPA) has widely been replaced in consumer products by analogs BPB, BPE, BPF, BPS, and BPAF. Recent studies have linked these substitutes to similar adverse health outcomes as BPA, including disruption of endocrine pathways in animal and human studies. We designed a novel MS method, developed specifically for this study, to capture the most relevant BPA alternatives, BPB, BPE, BPF, BPS, BPAF and 4-NP in human blood and urine to quantify potential in utero exposures. To our knowledge, this is the first study to explore in utero exposure to these BPA analogs and the first U.S. study to test for BPA in maternal/fetal pairs. The method was run on 30 paired maternal urine and fetal cord blood samples from mothers undergoing elective Caesarean sections. 90% of mothers and 77% of babies tested positive for at least one BP analog. 83% of mothers tested positive for BPAF, 60% for BPS, 57% for BPB, 17% for BPF and 7% for BPA. 57% of babies tested positive for BPAF and 50% for BPF. BPA and BPB were detected in one cord blood sample each. BPS was not detected in cord blood. BPE was not detected in any fetal cord blood or maternal urine samples. These findings demonstrate the pervasiveness of some BP analogs in pregnant women and their babies at birth.

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BACKGROUND: The prevalence of pediatric hormonal disorders and hormonally-sensitive cancers are rising. Chemicals including bisphenol A (BPA), phthalates, parabens, 4-nonylphenol (4NP) and triclosan have been linked to disruption of endocrine pathways and altered hormonal status in both animal and human studies. Additionally, changes in estrogen metabolism have been associated with pediatric endocrine disorders and linked to estrogen-dependent cancers. The main objective of the study was to measure the presence of these environmental chemicals in prepubescent children and assess the relationship between chemical metabolites and estrogen metabolism. METHODS: 50 subjects (25 male, 25 female) were recruited from the principal investigator's existing patient population at his pediatric primary care office. The first 5 boys and 5 girls in each age group (4 through 8 years old inclusive) who presented for annual examinations were included, as long as they were Tanner Stage I (prepubertal) on physical exam, without diagnosis of hormonally-related condition and/or cancer and able to give a urine sample. Urine samples were collected in glass containers for analysis of chemical and estrogen metabolites. Study kits and lab analysis were provided by Genova Diagnostics (Duluth, GA). Summary statistics for the concentrations of each chemical metabolite as well as estrogen metabolites were computed (minimum, maximum, median and inter-quartile range) for males only, for females only and for all subjects. Comparisons between groups (e.g. males v. females) were assessed using the nonparametric Wilcoxon test, since the data was skewed. The correlation between concentrations of chemical metabolites and estrogen metabolites in prepubescent children were examined by the Spearman's correlation coefficient (ρ). RESULTS: 100 % of subjects had detectable levels of at least five chemicals [corrected] in their urine, and 74 % had detectable levels of eight or more chemicals. 28 % of subjects had measurable levels of 4NP. No associations were found between the urine levels of chemicals and estrogen metabolites. CONCLUSIONS: Endocrine disrupting environmental chemicals were detected in all children in the study, with measurable levels of 4NP in nearly 1/3 of subjects. This is the first known published study of 4NP levels in American children. No associations were found between the urine levels of chemicals tested and estrogen metabolites. The presence of multiple chemicals in a majority of children's urine coupled with increasing prevalence of pediatric hormonal disorders warrants further research to elucidate potential causal mechanisms in pre- and post-pubertal children.

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BACKGROUND: Head lice most commonly affect children, ages 3 to 11. Concerns exist about the safety and efficacy of pesticide-based treatments. Published studies suggest dimethicone is a potentially safe and effective non-toxic treatment, but have not evaluated 100% dimethicone in a pediatric population. The objectives were to evaluate the efficacy and safety of 100% dimethicone for the treatment of head lice in children, monitored by school nurses. METHODS: This was a multi-site, open-label study of a 100% dimethicone gel for the treatment of head lice in a pediatric population. Children (ages 3-12) suspected of infestation with head lice were evaluated by school nurses at six schools and daycare programs in New York and New Jersey. Inclusion criteria were presence of at least three live lice, or one live louse and 10 viable eggs (eggs found within 1.27 cm of the scalp) and no use of any head lice treatment within four weeks of enrollment. Counts of live lice and viable eggs found in 58 subjects were tracked at baseline (Day 0) and on Day 1, Day 7, and Day 14 after treatment. RESULTS: After 1 day of treatment with 100% dimethicone, 98.30% of subjects were free of live lice and 55.20% were free of viable eggs. On day 14, 96.50% were still free of live lice, and 80.70% were free of viable eggs. All subjects were monitored by the school nurse at baseline and throughout the study period for adverse effects, including scalp erythema, excoriation, flaking and edema. There was one adverse event of skin irritation lasting 10 min, and no serious adverse events reported. Overall, scalp conditions improved from the baseline: 10 subjects (17.5%) reported mild to moderate scalp erythema on day 1, compared with only one subject (1.7%) on day 14; 8 subjects (14.3%) reported mild scalp excoriation on day 1, with none reporting on day 14. CONCLUSIONS: 100% dimethicone was found to be a safe and highly effective treatment for pediatric head lice. Because dimethicone avoids pesticide exposure and resistance issues, dimethicone should be considered as a first-line treatment for head lice. TRIAL REGISTRATION: NCT02213055 Date of registration: August 8, 2014. STANDARDS OF REPORTING: The CONSORT 2010 Checklist was consulted during the review of this manuscript. Please note that sections pertaining specifically to randomized controlled trials (RCT's) were not applicable.

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The SVZ (subventricular zone) contains neural stem cells and progenitors of various potentialities. Although initially parsed into A, B, and C cells, this germinal zone is comprised of a significantly more diverse population of cells. Here, we characterized a subset of postnatal PRPs (PDGF-AA-responsive precursors) that express functional PDGFα and ß receptors from birth to adulthood. When grown in PDGF-AA, dissociated neonatal rat SVZ cells divided to produce non-adherent clusters of progeny. Unlike the self-renewing EGF/FGF-2-responsive precursors that produce neurospheres, these PRPs failed to self-renew after three passages; therefore, we refer to the colonies they produce as spheroids. Upon differentiation these spheroids could produce neurons, type 1 astrocytes and oligodendrocytes. When maintained in medium supplemented with BMP-4 they also produced type 2 astrocytes. Using lineage tracing methods, it became evident that there were multiple types of PRPs, including a subset that could produce neurons, oligodendrocytes, and type 1 and type 2 astrocytes; thus some of these PRPs represent a unique population of precursors that are quatropotential. Spheroids also could be generated from the newborn neocortex and they had the same potentiality as those from the SVZ. By contrast, the adult neocortex produced less than 20% of the numbers of spheroids than the adult SVZ and spheroids from the adult neocortex only differentiated into glial cells. Interestingly, SVZ spheroid producing capacity diminished only slightly from birth to adulthood. Altogether these data demonstrate that there are PRPs that persist in the SVZ that includes a unique population of quatropotential PRPs.

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Racial residential segregation is associated with health inequalities in the USA, and one of the primary mechanisms is through influencing features of the neighborhood physical environment. To better understand how Black residential segregation might contribute to health risk, we examined retail redlining; the inequitable distribution of retail resources across racially distinct areas. A combination of visual and analytic methods was used to investigate whether predominantly Black census block groups in New York City had poor access to retail stores important for health. After controlling for retail demand, median household income, population density, and subway ridership, percent Black was associated with longer travel distances to various retail industries. Our findings suggest that Black neighborhoods in New York City face retail redlining. Future research is needed to determine how retail redlining may perpetuate health disparities and socioeconomic disadvantage.

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In this work we propose a fully Bayesian semiparametric method to estimate the intensity of an inhomogeneous spatial point process. The basic idea is to first convert intensity estimation into a Poisson regression setting via binning data points on a regular grid, and then model the log intensity semiparametrically using an adaptive version of Gaussian Markov random fields to smooth the corresponding counts. The inference is carried by an efficient Markov chain Monte Carlo simulation algorithm. Compared to existing methods for intensity estimation, for example, parametric modeling and kernel smoothing, the proposed estimator not only provides inference regarding the dependence of the intensity function on possible covariates, but also uses information from the data to adaptively determine the amount of smoothing at the local level. The effectiveness of using our method is demonstrated through simulation studies and an application to a rainforest dataset.

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OBJECTIVE: Social science and health literature have identified residential segregation as a critical factor in exposure to health-related resources, including food environments. Differential spatial patterning of food environments surrounding schools has significant import for youth. We examined whether fast food restaurants clustered around schools in New York City, and whether any observed clustering varied as a function of school type, school racial demographics, and area racial and socioeconomic demographics. METHOD: We geocoded fast food locations from 2006 (n=817) and schools from 2004-2005 (n=2096; public and private, elementary and secondary) in the five boroughs of New York City. A point process model (inhomogeneous cross-K function) examined spatial clustering. RESULTS: A minimum of 25% of schools had a fast food restaurant within 400 m. High schools had higher fast food clustering than elementary schools. Public elementary and high schools with large proportions of Black students or in block groups with large proportions of Black residents had higher clustering than White counterparts. Finally, public high schools had higher clustering than private counterparts, with 1.25 to 2 times as many restaurants than expected by chance. CONCLUSION: The results suggest that the geography of opportunity as it relates to school food environments is unequal in New York City.

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The high prevalence of obesity in African American populations may be due to the food environment in residential communities, and the density of fast food restaurants is an important aspect of the restaurant landscape in US cities. This study investigated racial and socioeconomic correlates of fast food density in New York City. We found that predominantly Black areas had higher densities of fast food than predominantly White areas; high-income Black areas had similar exposure as low-income Black areas; and national chains were most dense in commercial areas. The results highlight the importance of policy level interventions to address disparities in food environments as a key goal in obesity prevention efforts.

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We analyzed a very large set of molecular interactions that had been derived automatically from biological texts. We found that published statements, regardless of their verity, tend to interfere with interpretation of the subsequent experiments and, therefore, can act as scientific "microparadigms," similar to dominant scientific theories [Kuhn, T. S. (1996) The Structure of Scientific Revolutions (Univ. Chicago Press, Chicago)]. Using statistical tools, we measured the strength of the influence of a single published statement on subsequent interpretations. We call these measured values the momentums of the published statements and treat separately the majority and minority of conflicting statements about the same molecular event. Our results indicate that, when building biological models based on published experimental data, we may have to treat the data as highly dependent-ordered sequences of statements (i.e., chains of collective reasoning) rather than unordered and independent experimental observations. Furthermore, our computations indicate that our data set can be interpreted in two very different ways (two "alternative universes"): one is an "optimists' universe" with a very low incidence of false results (<5%), and another is a "pessimists' universe" with an extraordinarily high rate of false results (>90%). Our computations deem highly unlikely any milder intermediate explanation between these two extremes.

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