Prenatal diagnosis of sex chromosome aneuploidy: possible reasons for high rates of pregnancy termination

Sex chromosome aneuploidy (SCA), when detected in amniocentesis, is usually an unexpected result of a test carried out for another purpose. For most SCAs, the prognosis is milder and less predictable than trisomy 21, and therefore parents are faced with a difficult decision regarding the option of pregnancy termination. While studies from Europe and the USA report a declining trend in termination rates for SCA, our local experience is different. During the period 1989–1998, we diagnosed 60 SCA (including mosaics) in 20 106 amniocenteses (0.29%) and 48 (80%) of these pregnancies were terminated, a significantly higher proportion than has been reported in Europe and the USA. The present study shows that the difference between our experience and others' may be related to differences in cultural norms and values. Thirty women were interviewed, of whom 23 terminated and seven continued the pregnancy. Interview analyses showed that the main reason behind the decision to terminate the pregnancy was associated with the parents' fear of non-specific abnormality of the child, and concerns about abnormal sexual development. Although genetic counseling practised in our center aims to be non-directive, 56% of the women reported that the counseling was either directive towards termination, or that they at least felt that the counselor's attitude was pro-termination. Most women (93%) reported themselves as having come to terms with their decision. Copyright © 2001 John Wiley & Sons, Ltd.     

基于改进遗传算法率定水质参数的研究

提出一种新的优化准则函数,结合改进的加速遗传算法,能够求得全局最优的水质参数。实例计算结果表明,新算法具有较高的精确度和良好的优化性能。     

Real-time leak detection in oil pipelines using an Inverse Transient Analysis model

Based on Inverse Transient Analysis (ITA) method, a real-time leak detection method is proposed to capture leak location and the associated leak rate in oil pipe conveyance systems. In the proposed approach, location and flow rate of leak (if any), the fluid properties, as well as physical parameters of the system, are calculated in consecutive periods through minimizing the discrepancy between the calculated and measured flow parameters of the system. The method of characteristics is employed to numerically calculate the transient responses of the system and the genetic algorithm is utilized as the optimization engine. The proposed approach was applied to several real pipeline systems in which the required transient flow data are either directly collected from the field or fabricated with a third-party numerical software. Extensive numerical explorations were conducted to investigate the performance of the proposed method in real-time leak detection and to determine the extent to which field data errors, stemming from Supervisory Control and Data Acquisition (SCADA) systems and measurement equipment, affect the leak flow rate and location detectability of the proposed approach. The results show that the proposed approach provides promising results under a variety of transient and steady-state flow conditions even in the case with small leak flow rate of around 2% of the line rate. The results also reveal that the noises in the measurement data and the errors originated from SCADA systems do not significantly compromise the leak detectability of the proposed approach, confirming that the proposed approach can be utilized in practice.     

Chromosome damage in individuals exposed to heavy metals†

Testing the mutagenic activity of environmental pollutants has become an important area of modern environmental science and prophylactic medicine. The most suitable method for short‐term mutagenicity testing on man, at present, are chromosome studies on somatic cells of exposed individuals. Mutation types analyzed by such studies are of high practical relevance as indicator system of genetic damage induced in man under in vivo conditions. A rather large series of such studies has been dedicated to the action of heavy metals on individuals contacted with these metals under therapeutic, ecological or occupational conditions or by intoxication. Lead, cadmium, chromium, nickel, mercury, zinc and other metals as well as their compounds have been under study. Analyses of that kind, of course, are hampered by difficulties with the distinct estimation of the actual load as well as unclear conditions of exposition, e.g. simultaneous exposition to different metals.

Results obtained till now arouse some suspicion of a direct or indirect mutagenic activity in man by certain chromium and platinum compounds, arsenic, mercury, and combinations of lead with other heavy metals (cadmium, zinc, arsenic, antimony, etc.). Life style, above all smoking habits, well may act comutagenic. In most cases, however, mutagenic activity of metals and metal compounds apparently is clearly superposed by their toxic activity. In specific cases, chromosome studies also may contribute to discover sources of ecological exposition and to monitor occupational load by heavy metals.     

Long-term prediction model of rockburst in underground openings using heuristic algorithms and support vector machines

Rockburst possibility prediction is an important activity in many underground openings design and construction as well as mining production. Due to the complex features of rockburst hazard assessment systems, such as multivariables, strong coupling and strong interference, this study employs support vector machines (SVMs) for the determination of classification of long-term rockburst for underground openings. SVMs is firmly based on the theory of statistical learning algorithms, uses classification technique by introducing radial basis function (RBF) kernel function. The inputs of models are buried depth H, rocks’ maximum tangential stress σ_θ, rocks’ uniaxial compressive strength σ_c, rocks’ uniaxial tensile strength σ_t, stress coefficient σ_θ/σ_c, rock brittleness coefficient σ_c/σ_t and elastic energy index W_et. In order to improve predictive accuracy and generalization ability, the heuristic algorithms of genetic algorithm (GA) and particle swarm optimization algorithm (PSO) are adopted to automatically determine the optimal hyper-parameters for SVMs. The performance of hybrid models (GA + SVMs = GA-SVMs) and (PSO + SVMs = PSO-SVMs) have been compared with the grid search method of support vector machines (GSM-SVMs) model and the experimental values. It also gives variance of predicted data. A rockburst dataset, which consists of 132 samples, was employed to evaluate the current method for predicting rockburst grade, and the good results of overall success rate were obtained. The results indicated that the heuristic algorithms of GA and PSO can speed up SVMs parameter optimization search, the proposed method is robust model and might hold a high potential to become a useful tool in rockburst prediction research.     

Determining the community structure of the coral Seriatopora hystrix from hydrodynamic and genetic networks

The exchange of genetic information between coral reefs through the transport of larvae can be described in terms of networks that capture the linkages between distant populations. A key question arising from these networks is the determination of the highly connected modules (communities). Communities can be defined using genetic similarity or distance statistics between multiple samples but due to limited specimen sampling capacity the boundaries of the communities for the known coral reefs in the seascape remain unresolved. In this study we use the microsatellite composition of individual corals to compare sample populations using a genetic dissimilarity measure (F_ST) which is then used to create a complex network. This network involved sampling 1025 colonies from 22 collection sites and examining 10 microsatellites loci. The links between each sampling site were given a strength that was created from the pair wise F_ST values. The result is an undirected weighted network describing the genetic dissimilarity between each sampled population. From this network we then determined the community structure using a leading eigenvector algorithm within graph theory. However, given the relatively limited sampling conducted, the representation of the regional genetic structure was incomplete. To assist with defining the boundaries of the genetically based communities we also integrated the communities derived from a hydrodynamic and distance based networks. The hydrodynamic network, though more comprehensive, was of smaller spatial extent than our genetic sampling. A Bayesian Belief network was developed to integrate the overlapping communities. The results indicate the genetic population structure of the Great Barrier Reef and provide guidance on where future genetic sampling should take place to complete the genetic diversity mapping.     

Optimization of thermal performance of the parabolic trough solar collector systems based on GA-BP neural network model

The aim of this paper is to optimize the thermal performance (system output energy, thermal efficiency, and heat loss of cavity absorber) of parabolic trough solar collector (PTC) systems in order to improve its thermal performance, based on the genetic algorithm-back propagation (GA-BP) neural network model. There are a number of undefined problems, fuzzy or incomplete information and a complex thermal performance of the PTC systems. Therefore, the thermal performance prediction of the PTC systems based on GA-BP neural network model was developed. Subsequently, the metrics performances have been adopted to comprehensively understand the algorithm and evaluate the prediction accuracy. Results revealed that the GA-BP neural network model can be successfully used to predict the complex nonlinear relationship between the input variables and thermal performance of the PTC systems. The cosine effect has a great influence on the thermal performance; thereby the geometrical structure of the PTC systems was optimized. It was found that the optimized geometrical structure was beneficial to improve the thermal performance of the PTC system. In conclusion, the GA-BP neural network model has higher prediction accuracy than the other algorithm and it can be feasible and reliable.     

Optimal water quality monitoring network design for river systems

Typical tasks of a river monitoring network design include the selection of the water quality parameters, selection of sampling and measurement methods for these parameters, identification of the locations of sampling stations and determination of the sampling frequencies. These primary design considerations may require a variety of objectives, constraints and solutions. In this study we focus on the optimal river water quality monitoring network design aspect of the overall monitoring program and propose a novel methodology for the analysis of this problem. In the proposed analysis, the locations of sampling sites are determined such that the contaminant detection time is minimized for the river network while achieving maximum reliability for the monitoring system performance. Altamaha river system in the State of Georgia, USA is chosen as an example to demonstrate the proposed methodology. The results show that the proposed model can be effectively used for the optimal design of monitoring networks in river systems.     

中国对虾两个不同地理种群遗传结构的RAPD分析

采用ＲＡＰＤ技术对中国对虾中国黄渤海沿岸种群（ＨＢ）和朝鲜半岛西海岸种群（ＰＫ）的遗传多样性进行检测。２０个随机引物（ＯＰＤ系列）在二个种群中都扩增出１４８条ＤＮＡ片段,其中多态性片段数分别为５４条和６５条,多态性片段的比例分别为３６．４９％和４３．９２％,种群的平均杂合度分别为０．１９８１和０．２３７５。实验表明,中国对虾朝鲜半岛西海岸种群的遗传多样性要比中国黄渤海沿岸种群高,但二个种群都处于较