Time to initiation of symptomatic treatment in untreated Parkinson disease in the Parkinson progression marker initiative cohort: A 10-year follow up.

BACKGROUND: Initiation of symptomatic therapy in Parkinson disease is a disease progression milestone, and its prediction is important. Previous studies were limited in duration and number of variables included in their predictive models. OBJECTIVES: To identify predictors of time to initiation of symptomatic therapy in patients with PD not on treatment, using a large pool of candidate variables from the Parkinson's Progression Markers Initiative dataset, analyzed at ten years. METHODS: Kaplan Meier survival curve was used to estimate time to initiation of symptomatic treatment. Potential predictors included 33 baseline clinical, imaging, biofluid, and genetic biomarkers. Univariate Cox regression was used for variable selection, significant predictors subsequently entering a multivariate Cox proportional hazard model, which was further reduced using the Akaike Information Criterion into a final reduced model. RESULTS: Of 425 participants with Parkinson's Disease, 406 initiated symptomatic therapy at last follow up. The outcome was censored for 4.5% of the sample. The risk of initiating symptomatic therapy was 65% (95%CI 60-70%) within the first year from enrollment. Predictors included dopamine transporter SPECT, the Movement Disorders Society Unified Parkinson Disease Rating Scale, and anxiety (State Trait Anxiety Inventory). CONCLUSIONS: Baseline dopamine transporter SPECT specific binding ratio was found to be the most impactful predictor for time to initiation of symptomatic therapy in this 10-year follow up analysis of the Progressive Parkinson Markers Initiative cohort, when treatment status was known for 95.5% of the sample.

Evaluation of classification methods for identifying multiwalled carbon nanotubes collected on mixed cellulose ester filter media.

Enhanced darkfield microscopy (EDFM) and hyperspectral imaging (HSI) are being evaluated as a potential rapid screening modality to reduce the time-to-knowledge for direct visualisation and analysis of filter media used to sample nanoparticulate from work environments, as compared to the current analytical gold standard of transmission electron microscopy (TEM). Here, we compare accuracy, specificity, and sensitivity of several hyperspectral classification models and data preprocessing techniques to determine how to most effectively identify multiwalled carbon nanotubes (MWCNTs) in hyperspectral images. Several classification schemes were identified that are capable of classifying pixels as MWCNT(+) or MWCNT(-) in hyperspectral images with specificity and sensitivity over 99% on the test dataset. Functional principal component analysis (FPCA) was identified as an appropriate data preprocessing technique, testing optimally when coupled with a quadratic discriminant analysis (QDA) model with forward stepwise variable selection and with a support vector machines (SVM) model. The success of these methods suggests that EDFM-HSI may be reliably employed to assess filter media exposed to MWCNTs. Future work will evaluate the ability of EDFM-HSI to quantify MWCNTs collected on filter media using this classification algorithm framework using the best-performing model identified here - quadratic discriminant analysis with forward stepwise selection on functional principal component data - on an expanded sample set.

Multivariate analysis of spatial-temporal scales in melanoma prevalence.

Melanoma is a particularly deadly form of skin cancer arising from diverse biological and physical origins, making the characterization and quantification of relationships with recognized risk factors very complex. Melanoma has known associations with ultraviolet light exposure. Natural variations in solar electromagnetic irradiation, length of exposure, and intensity operate on different and therefore uncorrelated time scale frequencies. It is necessary to separate and investigate the principal components, such as the annual and solar cycle components, free from confounding influences. Kolmogorov-Zurbenko spatial filters applied to melanoma prevalence and environmental factors affecting solar irradiation exposure are able to identify and separate the independent space and time scale components of melanoma. Multidimensional analysis in space and time produces significantly improved model fit of what is in effect a linear regression of maps, or motion picture, in different time scales between melanoma rates and prominent factors. The resulting multivariate model coefficients of influence for each unique spatial-temporal melanoma component help quantify the relationships and are valuable to future research and prevention.

Occupational exposure to airborne nanomaterials: An assessment of worker exposure to aerosolized metal oxide nanoparticles in a semiconductor fab and subfab.

This occupational exposure assessment study characterized potential inhalation exposures of workers to engineered nanomaterials associated with chemical mechanical planarization wafer polishing processes in a semiconductor research and development facility. Air sampling methodology was designed to capture airborne metal oxide nanoparticles for characterization. The research team obtained air samples in the fab and subfab areas using a combination of filter-based capture methods to determine particle morphology and elemental composition and real-time direct-reading instruments to determine airborne particle counts. Filter-based samples were analyzed by electron microscopy and energy-dispersive x-ray spectroscopy while real-time particle counting data underwent statistical analysis. Sampling was conducted during worker tasks associated with preventive maintenance and quality control that were identified as having medium to high potential for inhalation exposure based on qualitative assessments. For each sampling event, data was collected for comparison between the background, task area, and personal breathing zone. Sampling conducted over nine months included five discrete sampling series events in coordination with on-site employees under real working conditions. The number of filter-based samples captured was: eight from worker personal breathing zones; seven from task areas; and five from backgrounds. A complementary suite of direct-reading instruments collected data for seven sample collection periods in the task area and six in the background. Engineered nanomaterials of interest (Si, Al, Ce) were identified in filter-based samples from all areas of collection, existing as agglomerates (>500 nm) and nanoparticles (100-500 nm). Particle counts showed an increase in number concentration above background during a subset of the job tasks, but particle counts in the task areas were otherwise not significantly higher than background. Additional data is needed to support further statistical analysis and determine trends; however, this initial investigation suggests that nanoparticles used or generated by the wafer polishing process become aerosolized and may be accessible for inhalation exposures by workers performing tasks in the subfab and fab. Additional research is needed to further quantify the degree of exposure and link these findings to related hazard research.

Occupational Exposure to Airborne Nanomaterials: An Assessment of Worker Exposure to Aerosolized Metal Oxide Nanoparticles in Semiconductor Wastewater Treatment.

This study characterized potential inhalation exposures of workers to nanometal oxides associated with industrial wastewater treatment processes in a semiconductor research and development facility. Exposure assessment methodology was designed to capture aerosolized engineered nanomaterials associated with the chemical mechanical planarization wafer polishing process that were accessible for worker contact via inhalation in the on-site wastewater treatment facility. The research team conducted air sampling using a combination of filter-based capture methods for particle identification and characterization and real-time direct-reading instruments for semi-quantitation of particle number concentration. Filter-based samples were analyzed using electron microscopy and energy-dispersive x-ray spectroscopy while real-time particle counting data underwent statistical analysis. Sampling conducted over 14 months included 5 discrete sampling series events for 7 job tasks in coordination with on-site employees. The number of filter-based samples captured for analysis by electron microscopy was: 5 from personal breathing zone, 4 from task areas, and 3 from the background. Direct-reading instruments collected data for 5 sample collection periods in the task area and the background, and 2 extended background collection periods. Engineered nanomaterials of interest (Si, Al, Ce) were identified by electron microscopy in filter-based samples from all areas of collection, existing as agglomerates (>500 nm) and nanoparticles (100 nm-500 nm). Particle counts showed an increase in number concentration during and after selected tasks above background. While additional data is needed to support further statistical analysis and determine trends, this initial investigation suggests that nanoparticles used or generated by chemical mechanical planarization become aerosolized and may be accessible for inhalation exposures by workers in wastewater treatment facilities. Additional research is needed to further quantify the level of exposure and determine the potential human health impacts.

Skin cancer, irradiation, and sunspots: the solar cycle effect.

Skin cancer is diagnosed in more than 2 million individuals annually in the United States. It is strongly associated with ultraviolet exposure, with melanoma risk doubling after five or more sunburns. Solar activity, characterized by features such as irradiance and sunspots, undergoes an 11-year solar cycle. This fingerprint frequency accounts for relatively small variation on Earth when compared to other uncorrelated time scales such as daily and seasonal cycles. Kolmogorov-Zurbenko filters, applied to the solar cycle and skin cancer data, separate the components of different time scales to detect weaker long term signals and investigate the relationships between long term trends. Analyses of crosscorrelations reveal epidemiologically consistent latencies between variables which can then be used for regression analysis to calculate a coefficient of influence. This method reveals that strong numerical associations, with correlations >0.5, exist between these small but distinct long term trends in the solar cycle and skin cancer. This improves modeling skin cancer trends on long time scales despite the stronger variation in other time scales and the destructive presence of noise.

Assessment of methods for prediction of human West Nile virus (WNV) disease from WNV-infected dead birds.

BACKGROUND: West Nile virus (WNV) is currently the leading cause of arboviral-associated encephalitis in the U.S., and can lead to long-term neurologic sequelae. Improvements in dead bird specimen processing time, including the availability of rapid field laboratory tests, allows reassessment of the effectiveness of using WNV-positive birds in forecasting human WNV disease. METHODS: Using New York State integrated WNV surveillance data from transmissions seasons in 2001-2003, this study determined which factors associated with WNV-positive dead birds are most closely associated with human disease. The study also addressed the 'delay' period between the distribution of the dead bird variable and the distribution of the human cases. In the last step, the study assessed the relative risk of contracting WNV disease for people who lived in counties with a 'signal' value of the predictor variable versus people who lived in counties with no 'signal' value of the predictor variable. RESULTS: The variable based on WNV-positive dead birds [(Positive/Tested)*(Population/Area)] was identified as the optimum variable for predicting WNV human disease at a county level. The delay period between distribution of the variable and human cases was determined to be approximately two weeks. For all 3 years combined, the risk of becoming a WNV case for people who lived in 'exposed' counties (those with levels of the positive dead bird variable above the signal value) was about 2 times higher than the risk for people who lived in 'unexposed' counties, but risk varied by year. CONCLUSION: This analysis develops a new variable based on WNV-positive dead birds, [(Positive/Tested)*(Population/Area)] to be assessed in future real-time studies for forecasting the number of human cases in a county. A delay period of approximately two weeks between increases in this variable and the human case onset was identified. Several threshold 'signal' values were assessed and found effective at indicating human case risk, although specific thresholds are likely to vary by region and surveillance system differences.

Evaluating the Effectiveness of Ozone Management Efforts in the Presence of Meteorological Variability.

It is difficult to assess the effectiveness of regulatory programs in improving ozone air quality in the presence of meteorological fluctuations. In this paper, techniques are presented that improve upon previous methods for moderating the effects of meteorology on ozone concentrations. This approach entails the use of the relations between ozone and meteorological variables to construct meteorologically adjusted ozone time series. To this end, the effectiveness and usefulness of various methods for separating time series of ozone and meteorological data into long-term (climate- and policy-related), seasonal (solar-induced), and short-term (weather-related) components are examined. Correlations between baseline components (sum of long-term and seasonal variations) of ozone and meteorological variables are then investigated independently of correlations between short-term components (weather effects) of ozone and meteorological variables. This allows us to account for the effects of the dominant meteorological variables on each time scale embedded in time series of ozone data. Ozone time series that are devoid of seasonal and climatic variations as well as weather-related fluctuations can then be constructed to detect and track changes in ozone due to the emission control policies implemented. The results of this study reveal that the combination of solar radiation and specific humidity performs best in filtering the seasonal and climatic variations from the baseline component of the ozone data. The combination of temperature and dew point depression performs best in moderating the weather-related effects on the short-term component of ozone data. This method is able to explain about 65% of the variance in ozone data through meteorological variables at several locations examined here.

Moderating the Influence of Meteorological Conditions on Ambient Ozone Concentrations.

Because ambient ozone concentrations are so strongly influenced by stochastic and seasonal variations, it is difficult to assess the effectiveness of regulatory controls in improving ambient ozone air quality. The purpose of this paper is to present a method for moderating the influence of meteorological fluctuations on ambient ozone levels. Techniques presented here account for temperature and other meteorological variables that affect ambient ozone concentrations. To this end, we have examined the correlation between several meteorological variables and ozone concentrations. In addition, we have evaluated trends in ozone time series after removing the effects of these variables on ozone concentrations. The results indicate that inclusion of two meteorological variables strengthens the relationship between ozone and meteorological effects. Moreover, the meteorologically-independent ozone time series at one of the locations studied had a significant trend that was not detected in temperature-independent ozone concentrations.