Estimating resource utilization demands in implementing statewide screening,brief intervention,and referral to treatment for alcohol-impaired drivers

Objectives: The Alcohol Use Disorders Identification Test (AUDIT) is used to assess the level of alcohol use/misuse and to inform the intensity of intervention delivered within screening, brief intervention, and referral to treatment (SBIRT) programs. Policy initiatives are recommending delivery of SBIRT within health care settings to reduce alcohol misuse and prevent alcohol-impaired driving. Recent reports are considering extending delivery of SBIRT to criminal justice settings. One consideration in implementing SBIRT delivery is the question of resource utilization; the amount of effort required in delivering the 4 different intensities of intervention in SBIRT: Alcohol education, simple advice, brief counseling and continued monitoring, and brief counseling and referral to specialist (from least to most intense in terms of delivery time, the skill level of the provider, and personnel resources).

Methods: In order to inform expectations about intervention intensity, this article describes the AUDIT scores from 982 adults recently arrested for alcohol-impaired driving. The distribution of scores is extrapolated to state rates for individuals arrested for alcohol-impaired driving by intervention level.

Results: Though alcohol education was the most common intervention category, about one quarter of the sample scored in a range corresponding with the more intensive interventions using the brief counseling, continued monitoring for ongoing alcohol use, and/or referral to specialist for diagnostic evaluation and treatment.

Conclusions: This article provides local distribution of AUDIT scores and state estimates for the number of individuals scoring in each level of risk (AUDIT risk zone) and corresponding intervention type. Routine criminal justice practice is well positioned to deliver alcohol screening, education, simple advice, and continued alcohol monitoring, making delivery of SBIRT feasible for the majority of alcohol-impaired drivers. Challenges to implementing the full range of SBIRT services include resource demands of brief counseling, identifying the appropriate providers within a criminal justice context, and availability of community providers for referral to diagnostic and specialty care. Solutions may vary by state due to differences in population density and incidence rates of alcohol-impaired driving.     

Effectiveness of Panama as an intercontinental land bridge for large mammals

Habitat fragmentation is a primary driver of wildlife loss, and establishment of biological corridors is a common strategy to mitigate this problem. A flagship example is the Mesoamerican Biological Corridor (MBC), which aims to connect protected forest areas between Mexico and Panama to allow dispersal and gene flow of forest organisms. Because forests across Central America have continued to degrade, the functioning of the MBC has been questioned, but reliable estimates of species occurrence were unavailable. Large mammals are suitable indicators of forest functioning, so we assessed their conservation status across the Isthmus of Panama, the narrowest section of the MBC. We used large-scale camera-trap surveys and hierarchical multispecies occupancy models in a Bayesian framework to estimate the occupancy of 9 medium to large mammals and developed an occupancy-weighted connectivity metric to evaluate species-specific functional connectivity. White-lipped peccary (Tayassu pecari), jaguar (Panthera onca), giant anteater (Myrmecophaga tridactyla), white-tailed deer (Odocoileus virginianus), and tapir (Tapirus bairdii) had low expected occupancy along the MBC in Panama. Puma (Puma concolor), red brocket deer (Mazama temama), ocelot (Leopardus pardalis), and collared peccary (Pecari tajacu), which are more adaptable, had higher occupancy, even in areas with low forest cover near infrastructure. However, the majority of species were subject to ≥1 gap that was larger than their known dispersal distances, suggesting poor connectivity along the MBC in Panama. Based on our results, forests in Darien, Donoso–Santa Fe, and La Amistad International Park are critical for survival of large terrestrial mammals in Panama and 2 areas need restoration.     

双风机并联机站局阻特性研究

为探究双风机并联机站局部阻力特性、完善机站局部阻力及其系数的计算公式,以实际矿井通风机站为工程背景,在分析机站流场能量损失机理的基础上,采用物理模型实验和数值计算相结合的方法,分析了机站入口分流、出口汇流的断面速度不均匀程度与机站阻力损失的关系,同时对不同宽高比的矩形断面进行多工况模拟。结果表明:断面速度不均匀性越大,速度分布重组造成的阻力损失越大;确定了机站局阻计算所需参数的合理测定位置,得出机站局阻系数计算时的综合影响系数Kc随风机工况增大而增大、随断面宽高比A增大而减小,并呈现较好的线性关系。研究结果可为多级机站通风系统通风阻力特性分析以及工程优化设计提供参考。     

基于模糊贝叶斯网络的煤气化职业健康风险分析

针对煤气化行业职业健康风险影响因素不确定及模糊的特点,建立了职业健康风险计算模型。该模型将模糊数学与贝叶斯网络相耦合,模拟事件概率,找出导致风险的主要因素。通过分析煤气化行业中存在的多种风险因素,应用问卷调查法和模糊集理论模拟了根节点的发生概率,得出职业健康风险概率的预测值;应用贝叶斯网络反向推理的功能计算根节点后验概率并排序,确定了薄弱环节。该模型不仅能解决概率缺失情况下的风险量化推算问题,定量进行职业健康风险评估,还可以实现关键因素的识别,并能有针对性地提出改进措施,为职业健康风险预防提供决策依据。     

Spatial modeling and prediction under stationary non-geometric range anisotropy

For modeling spatial processes, we propose a rich parametric class of stationary range anisotropic covariance structures that, when applied in R², greatly increases the scope of variogram contors. Geometric anisotropy, which provides the most common generalization of isotropy within stationarity, is a special case. Our class is built from monotonic isotropic correlation functions and special cases include the Matérn and the general exponential functions. As a result, our range anisotropic correlation specification can be attached to a second order stationary spatial process model, unlike ad hoc approaches to range anisotropy in the literature. We adopt a Bayesian perspective to obtain full inference and demonstrate how to fit the resulting model using sampling-based methods. In the presence of measurement error/microscale effect, we can obtain both the usual predictive as well as the noiseless predictive distribution. We analyze a data set of scallop catches under the general exponential range anisotropic model, withholding ten sites to compare the accuracy and precision of the standard and noiseless predictive distributions.     

On the latent state estimation of nonlinear population dynamics using Bayesian and non-Bayesian state-space models

Nonlinear state-space models have been increasingly applied to study population dynamics and data assimilation in environmental sciences. State-space models can account for process error and measurement error simultaneously to correct for the bias in the estimates of system state and model parameters. However, few studies have compared the performance of different nonlinear state-space models for reconstructing the state of population dynamics from noisy time series. This study compared the performance of the extended Kalman filter (EKF), unscented Kalman filter (UKF) and Bayesian nonlinear state-space models (BNSSM) through simulations. Synthetic population time series were generated using the theta logistic model with known parameters, and normally distributed process and measurement errors were introduced using the Monte Carlo simulations. At higher levels of nonlinearity, the UKF and BNSSM had lower root mean square error (RMSE) than the EKF. The BNSSM performed reliably across all levels of nonlinearity, whereas increased levels of nonlinearity resulted in higher RMSE of the EKF. The Metropolis–Hastings algorithm within the Gibbs algorithm was used to fit the theta logistic model to synthetic time series to estimate model parameters. The estimated posterior distribution of the parameter θ indicated that the 95% credible intervals included the true values of θ (=0.5 and 1.5), but did not include 1.0 and 0.0. Future studies need to incorporate the adaptive Metropolis algorithm to estimate unknown model parameters for broad applications of Bayesian nonlinear state-space models in ecological studies.     

Is boldness affected by group composition in young-of-the-year perch (Perca fluviatilis)?

In young-of-the-year perch (Perca fluviatilis), individuals within groups differed in the degree of boldness, estimated by habitat utilisation and feeding activity in visual contact with a potential predator. We looked at changes in individual behaviour in connection with change of group composition. During the first period, perch were randomly assigned to groups, and time spent in open habitat versus in vegetation and number of prey attacks were registered. The perch were then categorised into personality types (shy, bold, intermediate) according to their behaviour. During the second period, fish were observed when sorted into new groups, each containing only one personality type. Shy individuals showed the largest changes in behaviour, and increased both the time spent in the open and the number of prey attacks when placed into the new groups. Feeding activity in shy fish during the second period was affected by group composition during the first period. After regrouping, bold individuals decreased their time in the open, whereas intermediate individuals did not change behaviour. Time in the open habitat was, to some extent, influenced by the behaviour of the other members of the group, but number of prey attacks was not. The behaviour of fish of the different personality types we have defined in this study seemed to be based on innate traits, but also modified by the influence of other group members and by habituation to the environment.Communicated by J.Krause     

A Bayesian strategy for combining predictions from empirical and process-based models

We present a strategy for using an empirical forest growth model to reduce uncertainty in predictions made with a physiological process-based forest ecosystem model. The uncertainty reduction is carried out via Bayesian melding, in which information from prior knowledge and a deterministic computer model is conditioned on a likelihood function. We used predictions from an empirical forest growth model G-HAT in place of field observations of aboveground net primary productivity (ANPP) in a deciduous temperate forest ecosystem. Using Bayesian melding, priors for the inputs of the process-based forest ecosystem PnET-II were propagated through the model, and likelihoods for the PnET-II output ANPP were calculated using the G-HAT predictions. Posterior distributions for ANPP and many PnET-II inputs obtained using the G-HAT predictions largely matched posteriors obtained using field data. Since empirical growth models are often more readily available than extensive field data sets, the method represents a potential gain in efficiency for reducing the uncertainty of process-based model predictions when reliable empirical models are available but high-quality data are not.     

Active Adaptive Management for Conservation

Abstract:  Active adaptive management balances the requirements of management with the need to learn about the system being managed, which leads to better decisions. It is difficult to judge the benefit of management actions that accelerate information gain, relative to the benefit of making the best management decision given what is known at the time. We present a first step in developing methods to optimize management decisions that incorporate both uncertainty and learning via adaptive management. We assumed a manager can allocate effort to discrete units (e.g., areas for revegetation or animals for reintroduction), the outcome can be measured as success or failure (e.g., the revegetation in an area is successful or the animal survives and breeds), and the manager has two possible management options from which to choose. We further assumed that there is an annual budget that may be allocated to one or both of the two options and that the manager must decide on the allocation. We used Bayesian updating of the probability of success of the two options and stochastic dynamic programming to determine the optimal strategy over a specified number of years. The costs, level of certainty about the success of the two options, and the timeframe of management all influenced the optimal allocation of the annual budget. In addition, the choice of management objective had a large influence on the optimal decision. In a case study of Merri Creek, Melbourne, Australia, we applied the approach to determining revegetation strategies. Our approach can be used to determine how best to manage ecological systems in the face of uncertainty.     

RAPD扩增中商品酶体系对扩增产物的影响

随机选用了10条RAPD引物，采用3种商品TapDNA聚合酶体系，以5个不同物种的动植物总DNA为模板，进行RAPD扩增，实验发现，在对同一模板、采用同一引物进行的RAPD扩增中，仅仅因为使用了不同的商品TaqDNA聚合酶，获得的扩增产物差别极大；不同商品来源的PCR反应缓冲体系，对扩增产物也有一定影响，但相对较小，这说明，不同的商品酶体系对RAPD扩增产物影响很大，因此，影响RAPD扩增产物稳定性的一个关键因素是商品TaqDNA聚合酶本身；研究中前后一致地使用同一商品TaqDNA聚合酶体系，是成功进行RAPD分析的基础。