The relationship between exposure to polycyclic aromatic hydrocarbons and adult atopic dermatitis.

BACKGROUND: Polycyclic aromatic hydrocarbons are one of the major pathogenic components in air pollution. Previous studies have demonstrated an association between air pollution and atopic dermatitis. OBJECTIVE: We sought to explore the relationship between polycyclic aromatic hydrocarbons exposure and adult atopic dermatitis. METHODS: We prospectively recruited 23 adult patients with atopic dermatitis and 11 healthy controls. Plasma levels of inflammatory cytokines were determined using enzyme-linked immunosorbent assay. Expression levels of aryl hydrocarbon receptor, which mediates the effect of polycyclic aromatic hydrocarbons, and cytokines in peripheral blood nuclear cells (PBMCs) were measured using reverse transcription polymerase chain reaction. Urine levels of 16 polycyclic aromatic hydrocarbon metabolites were determined by gas chromatography- tandem mass spectrometry. RESULTS: Patients with atopic dermatitis had lower levels of interleukin (IL)-5 and IL-23, and lower PBMC messenger RNA expression levels of interferon-> than the healthy controls. Plasma levels of IL-22 were moderately and positively associated with the SCORAD index. Creatinine-corrected urine levels of 9-hydroxyfluorene and 2-hydroxyphenanthrene were elevated in the atopic dermatitis group. However the difference was not statistically significant after Bonferroni correction. CONCLUSIONS: Our results demonstrated that the polycyclic aromatic hydrocarbons fluorene and phenanthrene are potentially associated with the pathogenesis of atopic dermatitis in adults.

The NCA-1 and NCA-2 Ion Channels Function Downstream of Gq and Rho To Regulate Locomotion in Caenorhabditis elegans.

The heterotrimeric G protein Gq positively regulates neuronal activity and synaptic transmission. Previously, the Rho guanine nucleotide exchange factor Trio was identified as a direct effector of Gq that acts in parallel to the canonical Gq effector phospholipase C. Here, we examine how Trio and Rho act to stimulate neuronal activity downstream of Gq in the nematode Caenorhabditis elegans Through two forward genetic screens, we identify the cation channels NCA-1 and NCA-2, orthologs of mammalian NALCN, as downstream targets of the Gq-Rho pathway. By performing genetic epistasis analysis using dominant activating mutations and recessive loss-of-function mutations in the members of this pathway, we show that NCA-1 and NCA-2 act downstream of Gq in a linear pathway. Through cell-specific rescue experiments, we show that function of these channels in head acetylcholine neurons is sufficient for normal locomotion in C. elegans Our results suggest that NCA-1 and NCA-2 are physiologically relevant targets of neuronal Gq-Rho signaling in C. elegans.

Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques.

This study attempts to model the spatio-temporal dynamics of total phosphate (TP) concentrations along a river for effective hydro-environmental management. We propose a systematical modeling scheme (SMS), which is an ingenious modeling process equipped with a dynamic neural network and three refined statistical methods, for reliably predicting the TP concentrations along a river simultaneously. Two different types of artificial neural network (BPNN-static neural network; NARX network-dynamic neural network) are constructed in modeling the dynamic system. The Dahan River in Taiwan is used as a study case, where ten-year seasonal water quality data collected at seven monitoring stations along the river are used for model training and validation. Results demonstrate that the NARX network can suitably capture the important dynamic features and remarkably outperforms the BPNN model, and the SMS can effectively identify key input factors, suitably overcome data scarcity, significantly increase model reliability, satisfactorily estimate site-specific TP concentration at seven monitoring stations simultaneously, and adequately reconstruct seasonal TP data into a monthly scale. The proposed SMS can reliably model the dynamic spatio-temporal water pollution variation in a river system for missing, hazardous or costly data of interest.

Modeling water quality in an urban river using hydrological factors--data driven approaches.

Contrasting seasonal variations occur in river flow and water quality as a result of short duration, severe intensity storms and typhoons in Taiwan. Sudden changes in river flow caused by impending extreme events may impose serious degradation on river water quality and fateful impacts on ecosystems. Water quality is measured in a monthly/quarterly scale, and therefore an estimation of water quality in a daily scale would be of good help for timely river pollution management. This study proposes a systematic analysis scheme (SAS) to assess the spatio-temporal interrelation of water quality in an urban river and construct water quality estimation models using two static and one dynamic artificial neural networks (ANNs) coupled with the Gamma test (GT) based on water quality, hydrological and economic data. The Dahan River basin in Taiwan is the study area. Ammonia nitrogen (NH3-N) is considered as the representative parameter, a correlative indicator in judging the contamination level over the study. Key factors the most closely related to the representative parameter (NH3-N) are extracted by the Gamma test for modeling NH3-N concentration, and as a result, four hydrological factors (discharge, days w/o discharge, water temperature and rainfall) are identified as model inputs. The modeling results demonstrate that the nonlinear autoregressive with exogenous input (NARX) network furnished with recurrent connections can accurately estimate NH3-N concentration with a very high coefficient of efficiency value (0.926) and a low RMSE value (0.386 mg/l). Besides, the NARX network can suitably catch peak values that mainly occur in dry periods (September-April in the study area), which is particularly important to water pollution treatment. The proposed SAS suggests a promising approach to reliably modeling the spatio-temporal NH3-N concentration based solely on hydrological data, without using water quality sampling data. It is worth noticing that such estimation can be made in a much shorter time interval of interest (span from a monthly scale to a daily scale) because hydrological data are long-term collected in a daily scale. The proposed SAS favorably makes NH3-N concentration estimation much easier (with only hydrological field sampling) and more efficient (in shorter time intervals), which can substantially help river managers interpret and estimate water quality responses to natural and/or manmade pollution in a more effective and timely way for river pollution management.

Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysis.

We propose a systematical approach to assessing arsenic concentration in a river through: important factor extraction by a nonlinear factor analysis; arsenic concentration estimation by the neuro-fuzzy network; and impact assessment of important factors on arsenic concentration by the membership degrees of the constructed neuro-fuzzy network. The arsenic-contaminated Huang Gang Creek in northern Taiwan is used as a study case. Results indicate that rainfall, nitrite nitrogen and temperature are important factors and the proposed estimation model (ANFIS(GT)) is superior to the two comparative models, in which 50% and 52% improvements in RMSE are made over ANFIS(CC) and ANFIS(all), respectively. Results reveal that arsenic concentration reaches the highest in an environment of lower temperature, higher nitrite nitrogen concentration and larger one-month antecedent rainfall; while it reaches the lowest in an environment of higher temperature, lower nitrite nitrogen concentration and smaller one-month antecedent rainfall. It is noted that these three selected factors are easy-to-collect. We demonstrate that the proposed methodology is a useful and effective methodology, which can be adapted to other similar settings to reliably model water quality based on parameters of interest and/or study areas of interest for universal usage. The proposed methodology gives a quick and reliable way to estimate arsenic concentration, which makes good contribution to water environment management.

Reinforced two-step-ahead weight adjustment technique for online training of recurrent neural networks.

A reliable forecast of future events possesses great value. The main purpose of this paper is to propose an innovative learning technique for reinforcing the accuracy of two-step-ahead (2SA) forecasts. The real-time recurrent learning (RTRL) algorithm for recurrent neural networks (RNNs) can effectively model the dynamics of complex processes and has been used successfully in one-step-ahead forecasts for various time series. A reinforced RTRL algorithm for 2SA forecasts using RNNs is proposed in this paper, and its performance is investigated by two famous benchmark time series and a streamflow during flood events in Taiwan. Results demonstrate that the proposed reinforced 2SA RTRL algorithm for RNNs can adequately forecast the benchmark (theoretical) time series, significantly improve the accuracy of flood forecasts, and effectively reduce time-lag effects.

Dictyostelium gnt15 encodes a protein with similarity to LARGE and plays an essential role in development.

LARGE is a putative glycosyltransferase found to be mutated in mice with myodystrophy or patients with congenital muscular dystrophy. By homology searches, we identified in the Dictyostelium discoideum genome four open reading frames, i.e. gnt12-15, encoding proteins with sequence similarity to LARGE. Semi-quantitative RT-PCR analysis revealed distinct temporal expression patterns of the four gnt genes throughout Dictyostelium development. To explore the gene function, we performed targeted disruptions of gnt14 and gnt15. The gnt14(-) strains showed no obvious phenotypes. However, gnt15(-) cells grew slowly, changed in morphology, and displayed a developmental phenotype arresting at early stages. Compared with the wild type, gnt15(-) cells were more adhesive and exhibited altered levels of some surface adhesion molecules. Moreover, lectin-binding analysis demonstrated that gnt15 disruption affected profiles of membrane glycoproteins. Taken together, our data suggest that Gnt15 is essential for Dictyostelium development and may have a role in modulating cell adhesion and glycosylation.