Diagnosis,surveillance, and treatment of the anemic fetus using middle cerebral artery peak systolic velocity measurement

The in utero course of the anemic fetus has improved dramatically, owing to early diagnosis and cordocentesis transfusion. In utero invasive procedures such as amnio- and cordocentesis have become important modalities in the evaluation and treatment of anemic fetuses. However, they carry risks for both the mother and fetus. A valid and sensitive noninvasive means of following the anemic fetus is the evaluation of changes in the middle cerebral artery peak systolic flow velocity (MCA-PSV). This is a sensitive tool for both the evaluation of fetal anemia and response to treatment. Intracerebral vessels respond earliest to the fetal anemic state, and are readily accessible for ultrasound examination. We describe the methodology and evolving clinical applications of MCA-PSV measurement in the fetus, through an overview of the literature describing the development and application of MCA-PSV measurement in fetuses at risk of fetal anemia of various immune and nonimmune etiologies, illustrated by index cases from our center. MCA-PSV measurement is essential in the diagnosis, evaluation, and management of cases of fetal anemia. The use of this modality lessens the need for invasive procedures. The method is readily accessible and should be integrated into the repertoire of all obstetric ultrasound centers. Copyright © 2006 John Wiley & Sons, Ltd.     

Assessment of herbaceous plant habitats in water-constrained environments: predicting indirect effects with fuzzy logic

Herbaceous plant production plays a key role in determining the function of rangeland ecosystems in the semi-arid and Mediterranean regions. Therefore, assessment of herbaceous plant habitats is important for understanding the ecosystem functioning in these regions and for applied purposes, such as range management and land evaluation. This paper presents a model to assess herbaceous plant habitats in a basaltic stony environment in a Mediterranean region. The model is based on geographic information systems (GIS), remote sensing and fuzzy logic, while four indirect variables, which represent major characteristics of herbaceous habitats, are modeled: rock cover fraction; wetness index (WI); soil depth; and slope orientation (aspect). A linear unmixing model was used to measure rock cover on a per pixel basis using a Landsat TM summer image. The wetness index and local aspect were determined from digital elevation data with 25 m × 25 m pixel resolution, while soil data were gathered in a field survey. The modeling approach adopted here is process-based and assumes that water availability plays a crucial role in determining herbaceous plant production in Mediterranean and semi-arid environments. The model rules are based on fuzzy logic and are written based on the hypothesized water requirements of the herbaceous vegetation. The results show that on a polygon basis there is positive agreement between the model proposed here and previous mapping of the herbaceous habitats carried out in the field using traditional methods. Intrapolygon tests show that the use of a continuous raster data model and fuzzy logic principles provide an added value to traditional mapping. Moreover, herbaceous biomass measurements at two time intervals—mid- and peak winter season—corresponded with the habitat assessment predictions achieved using a new scenario that is proposed in this research. This scenario suggests that rockiness increases herbaceous production on south-facing slopes, while in other slope aspects the rock cover has lower impact on herbaceous growth. Due to its simplicity, the model suggested here can be used by planners and managers, to adjust range activities over large areas. The process-based approach should allow adaptation of the model to other regions more effectively than models that were formulated on a purely empirical basis. The model could also be used to study the relationship between water availability and ecosystem productivity on a regional scale.     

Active learning climate and employee errors: The moderating effects of personality traits

This study developed and tested a cross‐level model of the relationship between two components of active learning climate—enactive exploration and guided learning—and employee errors, and the moderating roles of the individual's traits of openness to experience and conscientiousness. The study examined the medical errors made by 142 resident physicians in 22 hospital departments. Results demonstrated significant interactions between climate and traits: when an individual had a low level of the trait of openness to experience, there was a curvilinear relationship between enactive exploration climate and number of errors. With a high level of openness to experience, the enactive exploration climate was positively associated with the number of errors up to a certain point. In addition, when an individual had a low level of conscientiousness, there was a negative association between guided learning climate and number of errors; with a high level of conscientiousness, there was a positive association between guided learning climate and number of errors. Thus, individual performance was better when the climate complemented the individual's lack of a trait. When the level of the individual's trait was high, the climate intensified the already pronounced trait up to an extent that impaired the individual's performance. Copyright © 2015 John Wiley & Sons, Ltd.