Changes in fetal heart rate pattern after intrauterine paracentesis in one twin with meconium peritonitis

We report a case of in utero paracentesis of ascites in a fetus with meconium peritonitis due to volvulus at 34 weeks which resulted in the correction of an abnormal fetal heart rate pattern and enabled vaginal delivery by preventing abdominal dystocia. The intrauterine intervention also helped to establish the diagnosis and potentially reduced the respiratory compromise after birth. Copyright © 2001 John Wiley & Sons, Ltd.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Persistent organic pollutants and heavy metals in adipose tissues of patients with uterine leiomyomas and the association of these pollutants with seafood diet, BMI, and age

Background, aim, and scope  

Persistent organic pollutants and heavy metals can cause diseases in women, however, the relationships of these pollutants and uterine leiomyomas (UL), which are non-cancerous tumors of the uterus, are unclear. This study focused on the quantification of organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), polybrominated diphenyl ethers (PBDEs), and heavy metals in subcutaneous and visceral fat obtained from patients with UL and in subcutaneous fat of a control group of women without UL to determine if there were any correlations between concentrations of persistent organic pollutants (POPs) and heavy metals and the incidence of UL.     

Improved land cover and emission factors for modeling biogenic volatile organic compounds emissions from Hong Kong

This paper describes a study of local biogenic volatile organic compounds (BVOC) emissions from the Hong Kong Special Administrative Region (HKSAR). An improved land cover and emission factor database was developed to estimate Hong Kong emissions using MEGAN, a BVOC emission model developed by Guenther et al. (2006). Field surveys of plant species composition and laboratory measurements of emission factors were combined with other data to improve existing land cover and emission factor data. The BVOC emissions from Hong Kong were calculated for 12 consecutive years from 1995 to 2006. For the year 2006, the total annual BVOC emissions were determined to be 12,400 metric tons or 9.82 × 10⁹ g C (BVOC carbon). Isoprene emission accounts for 72%, monoterpene emissions account for 8%, and other VOCs emissions account for the remaining 20%. As expected, seasonal variation results in a higher emission in the summer and a lower emission in the winter, with emission predominantly in day time. A high emission of isoprene occurs for regions, such as Lowest Forest-NT North, dominated by broadleaf trees. The spatial variation of total BVOC is similar to the isoprene spatial variation due to its high contribution. The year to year variability in emissions due to weather was small over the twelve-year period (?1.4%, 2006 to 1995 trendline), but an increasing trend in the annual variation due to an increase in forest land cover can be observed (+7%, 2006 to 1995 trendline). The results of this study demonstrate the importance of accurate land cover inputs for biogenic emission models and indicate that land cover change should be considered for these models.     

Acid Particles and the Tracheobronchial Region of the Respiratory System—An “Irritation-Signaling” Model for Possible Health Effects

This paper explores some detailed mechanistic hypotheses for the possible action of acid particles on the tracheobronchial region of the human respiratory system. Because of the buffering capacity and volume of mucus produced per day it appears doubtful that ordinary ambient exposures to acid particles could markedly change the overall pH of tracheobronchial mucus considered as a whole. However it is possible that individual acidic particles could contain enough acid to deliver localized “irritant signals” that could be the triggers for enhanced mucus secretion and cell division in sensitive portions of the bronchial tree, and thereby contribute to the processes involved in chronic bronchitis.

Depending on the exact pH depression required for a “signal” to be perceived by the tracheobronchial epithelium, the acid content of the incoming particles per unit weight, and the effect of neutralization by ammonia in the upper respiratory tract, the minimum size of an acidic particle required to deliver a perceptible signal might range from about 0.4 to 0.7 microns for portions of the epithelium that are frequently swept by 4-micron mucus droplets. (For unprotected epithelium, however, it is conceivable that the minimum effective size for acid particles could be less.) Since particle number per unit weight declines dramatically with increasing particle size, the most potent fraction of particles in terms of signals delivered per μg/m³is likely to be just above the minimum size that is needed to produce an effective signal. The model developed here makes predictions of the relative potency of particles of different size and acid delivery capacity that could be tested in both experimental animal systems and human epidemiological studies.     

Effect of Moisture on the Pollutant Concentration Measurement

The effects of removing moisture from gaseous samples on the measurement of pollutant concentration were studied. Simple mathematical analysis was conducted to derive the formula for correcting the measured results. It was found that the error increases with increase in relative humidity or increase in temperature. The error may become significant under extreme conditions.