First-trimester fetal sex determination in maternal serum using real-time PCR

An Erratum has been published for this article in Prenatal Diagnosis 22(13) 2002, 1241. Fetal sex prediction can be achieved using PCR targeted at the SRY gene by analysing cell-free fetal DNA in maternal serum. Unfortunately, the results reported to date show a lack of sensitivity, especially during the first trimester of pregnancy. Therefore, determination of fetal sex by maternal serum analysis could not replace karyotype analysis following chorionic villus sampling. A new highly sensitive real-time PCR was developped to detect an SRY gene sequence in maternal serum. Analysis was performed on 121 pregnant women during the first trimester of pregnancy (mean gestational age: 11.8 weeks). Among them, 51 had at least one previous male-bearing pregnancy. Results were compared with fetal sex. SRY PCR analysis of maternal serum was in complete concordance with fetal sex. Among the 121 pregnant women, 61 were bearing a male fetus and 60 a female fetus. No false-negative results were observed. Furthermore, no false-positive results occurred, even though 27 women carrying a female fetus during the current pregnancy had at least one previous male-bearing pregnancy. This study demonstrates that a reliable, non-invasive sex determination can be achieved by PCR analysis of maternal serum during the first trimester of pregnancy. This non-invasive approach for fetal sex prediction should have great implications in the management of pregnant women who are carriers of an X-linked genetic disorder. Prenatal diagnosis might thus be performed for male fetuses only, avoiding invasive procedures and the risk of the loss of female fetuses. Copyright © 2001 John Wiley & Sons, Ltd.     

Knowledge, values, uses and management of the Araucaria araucana forest by the indigenous Mapuche Pewenche people: A basis for collaborative natural resource management in southern Chile

One of the most important endemic tree species of Chile and at the same time one of the most endangered ones is Araucaria araucana (Mol.) C. Koch, the monkey‐puzzle tree. It grows in the Andes Mountains, homeland of the indigenous Mapuche Pewenche people who depend on this tree. This paper is based on field research that investigated the ecological knowledge, uses and management of the Araucaria araucana forest by indigenous Mapuche Pewenche people based on the socio‐cultural, spiritual and ecological relationships they have with the Araucaria forest, to find out how indigenous people and their knowledge could contribute to sustainable Araucaria forest management. A Mapuche Pewenche community located in the IX region of Chile contributed to this study. Based on the analyses this paper illustrates the nature of indigenous ecological knowledge of Araucaria araucana on the one hand, and its utility in native forest management on the other. The research shows that the Mapuche Pewenche hold ecological knowledge and conduct practices to manage their Araucaria forest in a balanced way. They conserve and use forest biodiversity at one and the same time. This paper provides recommendations for sustainable Araucaria forest management and conservation strategies ex‐situ and in‐situ incorporating indigenous knowledge and scientific knowledge and for promoting a collaborative natural resources management.     

The role of digital data entry in participatory environmental monitoring

Many argue that monitoring conducted exclusively by scientists is insufficient to address ongoing environmental challenges. One solution entails the use of mobile digital devices in participatory monitoring (PM) programs. But how digital data entry affects programs with varying levels of stakeholder participation, from nonscientists collecting field data to nonscientists administering every step of a monitoring program, remains unclear. We reviewed the successes, in terms of management interventions and sustainability, of 107 monitoring programs described in the literature (hereafter programs) and compared these with case studies from our PM experiences in Australia, Canada, Ethiopia, Ghana, Greenland, and Vietnam (hereafter cases). Our literature review showed that participatory programs were less likely to use digital devices, and 2 of our 3 more participatory cases were also slow to adopt digital data entry. Programs that were participatory and used digital devices were more likely to report management actions, which was consistent with cases in Ethiopia, Greenland, and Australia. Programs engaging volunteers were more frequently reported as ongoing, but those involving digital data entry were less often sustained when data collectors were volunteers. For the Vietnamese and Canadian cases, sustainability was undermined by a mismatch in stakeholder objectives. In the Ghanaian case, complex field protocols diminished monitoring sustainability. Innovative technologies attract interest, but the foundation of effective participatory adaptive monitoring depends more on collaboratively defined questions, objectives, conceptual models, and monitoring approaches. When this foundation is built through effective partnerships, digital data entry can enable the collection of more data of higher quality. Without this foundation, or when implemented ineffectively or unnecessarily, digital data entry can be an additional expense that distracts from core monitoring objectives and undermines project sustainability. The appropriate role of digital data entry in PM likely depends more on the context in which it is used and less on the technology itself.     

The Role of Disaggregation of Asset Values in Flood Loss Estimation: A Comparison of Different Modeling Approaches at the Mulde River,Germany

In loss estimation there is a spatial mismatch of hazard data that are commonly modeled on an explicit raster level and exposure data that are often available only for aggregated administrative units. Usually disaggregation methods that use ancillary information to distribute lumped exposure data in a finer spatial resolution help to bridge this gap. However, the actual influence of different mapping techniques and ancillary data on the final loss estimation has not been analyzed yet. In this paper three methods are applied to disaggregate residential building assets using two kinds of land use/land cover (LULC) data. The resulting disaggregated assets are validated and compared using census data of the residential building number on the community and constituency level. In addition, the disaggregated assets are taken to estimate residential building losses due to the flood in August 2002 in 21 municipalities on the River Mulde in Saxony, Germany. Losses are calculated with the help of four loss models. In general, disaggregation helps to decrease the error variance within the loss estimation. It must, however, be stated that the application of sophisticated disaggregation methods does not lead to significant improvements compared to the straightforward binary method. Therefore more effort should instead be put into the provision of high-resolution LULC data. Finally, the remaining uncertainties in loss estimation are high and demand further improvements in all modeling aspects.