Blood-based biomarkers in the maternal circulation associated with fetal growth restriction

Fetal growth restriction (FGR) is associated with threefold to fourfold increased risk of stillbirth. Identifying FGR, through its commonly used surrogate—the small-for-gestational-age (SGA, estimated fetal weight and/or abdominal circumference <10th centile) fetus—and instituting fetal surveillance and timely delivery decrease stillbirth risk. Methods available to clinicians for antenatal identification of SGA fetuses have surprisingly poor sensitivity. About 80% of cases remain undetected. Measuring the symphysis-fundal height detects only 20% of SGA fetuses, and even universal third trimester ultrasound detects, at best, 57% of those born SGA. There is an urgent need to find better ways to identify this at-risk cohort. This review summarises efforts to identify molecular biomarkers (proteins, metabolites, or ribonucleic acids) that could be used to better predict FGR. Most studies examining potential biomarkers to date have utilised case-control study designs without proceeding to validation in independent cohorts. To develop a robust test for FGR, large prospective studies are required with a priori validation plans and cohorts. Given that current clinical care detects 20% of SGA fetuses, even a screening test with ≥60% sensitivity at 90% specificity could be clinically useful, if developed. This may be an achievable aspiration. If discovered, such a test may decrease stillbirth.     

爱尔兰共和国境内的湖泊调查:水化学特性和酸敏感性

1997年在爱尔兰共和国进行了一项湖泊调查活动,本活动是确定并则绘临界负载的一个国家项目的一部分内容.共选取了200个湖泊作为调查的对象,约占湖泊总数的3.3‰.这个国家中的多数湖泊位于沿海岸带边缘的偏僻、高海拔、对酸具有敏感性的地区.在湖泊化学中来自海洋的物质的输入占据了主导地位.由于在大部分集水区中存在有机土壤,约有一半的湖泊中溶解有机碳(DOC)的含量大于5mg/L.在一半的湖泊中,非海洋硫酸盐的浓度处于背景水平上(＜20μeq/L).在7‰的抽查湖泊(13个湖泊)中发现临界负载超量.但由于海盐和有机酸之间的相互干扰,对临界负载的计算仍存在着不稳定性.在这种情况下如果进行精确的估计,就需要借助于长期的湖泊与沉积化学.     

Performance of Greater Sage-Grouse Models for Conservation Assessment in the Interior Columbia Basin, U.S.A.

Abstract: Valid modeling of habitats and populations of Greater Sage-Grouse ( Centrocercus urophasianus) is a critical management need because of increasing concern about population viability. Consequently, we evaluated the performance of two models designed to assess landscape conditions for Greater Sage-Grouse across 13.6 million ha of sagebrush steppe in the interior Columbia Basin and adjacent portions of the Great Basin of the western United States (referred to as the basin). The first model, the environmental index model, predicted conditions at the scale of the subwatershed (mean size of approximately 7800 ha) based on inputs of habitat density, habitat quality, and effects of human disturbance. Predictions ranged on a continuous scale from 0 for lowest environmental index to 2 for optimal environmental index. The second model, the population outcome model, predicted the composite, range-wide conditions for sage grouse based on the contribution of environmental index values from all subwatersheds and measures of range extent and connectivity. Population outcomes were expressed as five classes (A through E) that represented a gradient from continuous, well-distributed populations (outcome A) to sparse, highly isolated populations with a high likelihood of extirpation (outcome E). To evaluate performance, we predicted environmental index values and population outcome classes in areas currently occupied by sage grouse versus areas where extirpation has occurred. Our a priori expectations were that models should predict substantially worse environmental conditions ( lower environmental index) and a substantially higher probability of extirpation ( lower population outcome class) in extirpated areas. Results for both models met these expectations. For example, a population outcome of class E was predicted for extirpated areas, as opposed to class C for occupied areas. These results suggest that our models provided reliable landscape predictions for the conditions tested. This finding is important for conservation planning in the basin, where the models were used to evaluate management of federal lands for sage grouse.     

Tropical forest soil microbial communities couple iron and carbon biogeochemistry

We report that iron-reducing bacteria are primary mediators of anaerobic carbon oxidation in upland tropical soils spanning a rainfall gradient (3500-5000 mm/yr) in northeast Puerto Rico. The abundant rainfall and high net primary productivity of these tropical forests provide optimal soil habitat for iron-reducing and iron-oxidizing bacteria. Spatially and temporally dynamic redox conditions make iron-transforming microbial communities central to the belowground carbon cycle in these wet tropical forests. The exceedingly high abundance of iron-reducing bacteria (up to 1.2 x 10(9) cells per gram soil) indicated that they possess extensive metabolic capacity to catalyze the reduction of iron minerals. In soils from the higher rainfall sites, measured rates of ferric iron reduction could account for up to 44% of organic carbon oxidation. Iron reducers appeared to compete with methanogens when labile carbon availability was limited. We found large numbers of bacteria that oxidize reduced iron at sites with high rates of iron reduction and large numbers of iron reducers. The coexistence of large populations of iron-reducing and iron-oxidizing bacteria is evidence for rapid iron cycling between its reduced and oxidized states and suggests that mutualistic interactions among these bacteria ultimately fuel organic carbon oxidation and inhibit CH4 production in these upland tropical forests.     

A comparison of three empirically based, spatially explicit predictive models of residential soil Pb concentrations in Baltimore, Maryland, USA: understanding the variability within cities

In many older US cities, lead (Pb) contamination of residential soil is widespread; however, contamination is not uniform. Empirically based, spatially explicit models can assist city agencies in addressing this important public health concern by identifying areas predicted to exceed public health targets for soil Pb contamination. Sampling of 61 residential properties in Baltimore City using field portable X-ray fluorescence revealed that 53 % had soil Pb that exceeded the USEPA reportable limit of 400 ppm. These data were used as the input to three different spatially explicit models: a traditional general linear model (GLM), and two machine learning techniques: classification and regression trees (CART) and Random Forests (RF). The GLM revealed that housing age, distance to road, distance to building, and the interactions between variables explained 38 % of the variation in the data. The CART model confirmed the importance of these variables, with housing age, distance to building, and distance to major road networks determining the terminal nodes of the CART model. Using the same three predictor variables, the RF model explained 42 % of the variation in the data. The overall accuracy, which is a measure of agreement between the model and an independent dataset, was 90 % for the GLM, 83 % for the CART model, and 72 % for the RF model. A range of spatially explicit models that can be adapted to changing soil Pb guidelines allows managers to select the most appropriate model based on public health targets.     

Postdispersal seed predation limits the abundance of a long-lived perennial forb (Lithospermum ruderale)

Loss of seeds to consumers is common in plant communities, but the degree to which these losses influence plant abundance or population growth is often unclear. This is particularly the case for postdispersal seed predation by rodents, as most studies of rodent seed predation have focused on the sources of spatiotemporal variation in seed loss but not quantified the population consequences of this loss. In previous work we showed that seed predation by deer mice (Peromyscus maniculatus) substantially reduced seedling recruitment and establishment of Lithospermum ruderale (Boraginaceae), a long-lived perennial forb. To shed light on how rodent seed predation and the near-term effects on plant recruitment might influence longer-term patterns of L. ruderale population growth, we combined experimental results with demographic data in stage-based population models. Model outputs revealed that rodent seed predation had a significant impact on L. ruderale population growth rate (lambda). With the removal of postdispersal seed predation, the projected population growth rates increased between 0.06 and 0.12, depending on site (mean deltalambda across sites = 0.08). Seed predation shifted the projected stable stage distribution of populations from one with a high proportion of young plants to one in which larger adult size classes dominate. Elasticities of vital rates also changed, with germination and growth of seedlings and young plants becoming more important with the removal of seed predation. Simulations varying the magnitude of seed predation pressure while holding other vital rates constant showed that seed predation could lower lambda even if only 40% of available seeds were consumed. These results demonstrate that rodent granivory can be a potent force limiting the abundance of a long-lived perennial forb.     

On-board safety monitoring systems for driving: review, knowledge gaps, and framework

IntroductionFatal highway incidents remain the leading type of fatal work-related event, carrying tremendous personal, social, and economic costs. While employers with a fixed worksite can observe and interact directly with workers in an effort to promote safety and reduce risk, employers with workers who operate a motor vehicle as part of their job have fewer options. New technologies such as on-board safety monitoring systems offer the potential to further improve safety. These technologies allow vehicle owners to collect safety-specific information related to a driver's on-the-road behavior and performance. While many such devices are being developed and implemented in both commercial fleets and private vehicles, the scientific examination of these devices has lagged by comparison.MethodIn the current paper, we: (a) describe the general features and functionality of current generations of on-board monitoring devices and how they might impact various driver behaviors; (b) review the current state of scientific knowledge specific to on-board devices; (c) discuss knowledge gaps and potential areas for future research, borrowing from the related domain of computer-based electronic performance monitoring (EPM); and (d) propose a framework that can be used to explore some of the human-system interactions pertaining to monitoring systems.Impact on IndustryMotor vehicle crashes can carry tremendous costs for employers, in terms of injury, disability, and loss of potentially productive work years. New technologies can offer tremendous benefits in terms of promoting safer on-the-road behaviors.     

Both texting and eating are associated with impaired simulated driving performance

Objective: Distracted driving is a known contributor to traffic accidents, and many states have banned texting while driving. However, little is known about the potential accident risk of other common activities while driving, such as eating. The objective of the current study was to examine the adverse impact of eating/drinking behavior relative to texting and nondistracted behaviors on a simulated driving task. Methods: A total of 186 participants were recruited from undergraduate psychology courses over 2 semesters at Kent State University. We utilized the Kent Multidimensional Assessment Driving Simulation (K-MADS) to compare simulated driving performance among participants randomly assigned to texting (N = 45), eating (N = 45), and control (N = 96) conditions. Multivariate analyses of variance (MANOVA) were conducted to examine between-group differences on simulated driving indices. Results: MANOVA analyses indicated that groups differed in simulated driving performance, F(14, 366) = 7.70, P < .001. Both texting and eating produced impaired driving performance relative to controls, though these behaviors had approximately equal effect. Specifically, both texting and eating groups had more collisions, pedestrian strikes, and center line crossings than controls. In addition, the texting group had more road edge excursions than either eating or control participants and the eating group missed more stop signs than controls. Conclusions: These findings suggest that both texting and eating are associated with poorer simulated driving performance. Future work is needed to determine whether these findings generalize to real-world driving and the development of strategies to reduce distracted driving.