Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders.

BACKGROUND: Magnetic resonance imaging provides noninvasive tools to investigate alcohol use disorder (AUD) and nicotine use disorder (NUD) and neural phenotypes for genetic studies. A data-driven transdiagnostic approach could provide a new perspective on the neurobiology of AUD and NUD. METHODS: Using samples of individuals with AUD (n = 140), individuals with NUD (n = 249), and healthy control participants (n = 461) from the UK Biobank, we integrated clinical, neuroimaging, and genetic markers to identify biotypes of AUD and NUD. We partitioned participants with AUD and NUD based on resting-state functional connectivity (FC) features associated with clinical metrics. A multitask artificial neural network was trained to evaluate the cluster-defined biotypes and jointly infer AUD and NUD diagnoses. RESULTS: Three biotypes-primary NUD, mixed NUD/AUD with depression and anxiety, and mixed AUD/NUD-were identified. Multitask classifiers incorporating biotype knowledge achieved higher area under the curve (AUD: 0.76, NUD: 0.74) than single-task classifiers without biotype differentiation (AUD: 0.61, NUD: 0.64). Cerebellar FC features were important in distinguishing the 3 biotypes. The biotype of mixed NUD/AUD with depression and anxiety demonstrated the largest number of FC features (n = 5), all related to the visual cortex, that significantly differed from healthy control participants and were validated in a replication sample (p < .05). A polymorphism in TNRC6A was associated with the mixed AUD/NUD biotype in both the discovery (p = 7.3 × 10-5) and replication (p = 4.2 × 10-2) sets. CONCLUSIONS: Biotyping and multitask learning using FC features can characterize the clinical and genetic profiles of AUD and NUD and help identify cerebellar and visual circuit markers to differentiate the AUD/NUD group from the healthy control group. These markers support a new growing body of literature.

Chemical and stable carbon isotopic characterization for PAHs in aerosol emitted from two indoor sources.

Polycyclic aromatic hydrocarbons (PAHs) are with great environmental concerns due to their toxic, mutagenic, and carcinogenic properties. The harmful effects caused by PAHs emitted from indoor sources may be more direct and serious. In this study, PAHs in aerosol from two typical indoor sources, the cooking fume (CF) and environmental tobacco smoke (ETS) were collected by simulation emission in a chamber. Eighteen PAHs were analyzed by GC/MS and GC/C/IRMS. The chemical profiles of these two sources were described. Results indicated that Fluoranthene, Pyrene and Fluorene in CF, and Fluorene, Phenanthrene, Indeno(1,2,3-cd)pyrene and Benz(a)anthracene in ETS, relative quantity of which were variable in a smaller range, can be regarded as tracers of indoor PAHs sources. There are distinct differences among the ratios of Benz(a)anthracene/Chrysene, Benzo(e)pyrene/Benzo(a)pyrene and Indeno(1,2,3-cd)pyrene/Benzo(g,h,i)perylene between CF and EST. Distribution of delta(13)C of individual PAHs in ETS samples ranged from -21.76 per thousand to -29.32 per thousand, wider than that in CF samples (-22.94 per thousand to -28.39 per thousand). The delta(13)C of Phenanthrene, Benz(a)anthracene, Benzo(b)fluoranthene, Benzo(k)fluoranthene and Indeno(1,2,3-cd)pyrene between these two sources showed great differences. The (13)C was enriched in low molecule weight compounds of CF samples and in high molecule weight compounds of ETS samples.

Health risk assessment for traffic policemen exposed to polycyclic aromatic hydrocarbons (PAHs) in Tianjin, China.

In China, traffic policemen have to stand for several hours a day at the road intersections with high vehicle flows. To assess their exposure to airborne carcinogenic polycyclic aromatic hydrocarbons (PAHs) during their working time, a preliminary study was conducted to measure the personal exposure level to PAHs. And a probabilistic incremental lifetime cancer risk (ILCR) model together with the benzo[a]pyrene (BaP) toxic equivalents (BaP(eq)) method was used to conduct health risk assessment. Personal exposure monitors (PEM) were carried by traffic policemen to collect PM10 samples during their daily work in Tianjin, China. Meanwhile, PM100 samples were collected at the roadsides and on campus of Nankai University as comparison. PAHs species were quantitatively analyzed by GC/MS. Experimental results showed that the concentrations of total PAHs, BaP and BaP(eq) were much higher at the road intersections (867.5, 26.2, 82.4 ng m(-3)), where the traffic policemen stand during their work time, than those at the roadsides (46.6, 1.5, 5.7 ng m(-3)), and on campus (19.5, 0.7, 2.4 ng m(-3)). According to the risk assessment results, the occupational risk falls within the range from 10(-6) to 10(-3). On the basis of sensitivity analysis results, further research should be directed to give better characterization of the yearly concentration distribution of PAHs and the cancer slope factor (CSF) of BaP in order to improve the accuracy of the health risk assessment.

Boiler Briquette Coal versus Raw Coal: Part I-Stack Gas Emissions.

Stack gas emissions were characterized for a steam-generating boiler commonly used in China. The boiler was tested when fired with a newly formulated boiler briquette coal (BB-coal) and when fired with conventional raw coal (R-coal). The stack gas emissions were analyzed to determine emission rates and emission factors and to develop chemical source profiles. A dilution source sampling system was used to collect PM on both Teflon membrane filters and quartz fiber filters. The Teflon filters were analyzed gravimetrically for PM10 and PM2.5 mass concentrations and by X-ray fluorescence (XRF) for trace elements. The quartz fiber filters were analyzed for organic carbon (OC) and elemental carbon (EC) using a thermal/optical reflectance technique. Sulfur dioxide was measured using the standard wet chemistry method. Carbon monoxide was measured using an Orsat combustion analyzer. The emission rates of the R-coal combustion (in kg/hr), determined using the measured stack gas concentrations and the stack gas emission rates, were 0.74 for PM10, 0.38 for PM25, 20.7 for SO2, and 6.8 for CO, while those of the BB-coal combustion were 0.95 for PM10, 0.30 for PM2 5, 7.5 for SO2, and 5.3 for CO. The fuel-mass-based emission factors (in g/kg) of the R-coal, determined using the emission rates and the fuel burn rates, were 1.68 for PM10, 0.87 for PM25, 46.7 for SO2, and 15 for CO, while those of the BB-coal were 2.51 for PM10, 0.79 for PM2.5, 19.9 for SO2, and 14 for CO. The task-based emission factors (in g/ton steam generated) of the R-coal, determined using the fuel-mass-based emission factors and the coal/ steam conversion factors, were 0.23 for PM10, 0.12 for PM2.5, 6.4 for SO2, and 2.0 for CO, while those of the BB-coal were 0.30 for PM10, 0.094 for PM2.5, 2.4 for SO2, and 1.7 for CO. PM10 and PM2.5 elemental compositions are also presented for both types of coal tested in the study.