Noninvasive prenatal testing creates an opportunity for antenatal treatment of Down syndrome

Trisomy 21 (T21) is the most common autosomal aneuploidy that is associated with intellectual disability. It is the focus of many prenatal screening programs across the globe. Pregnant women who receive a prenatal diagnosis of T21 in their fetus currently have the option of continuing or terminating their pregnancy, but no fetal treatment is available. In this paper, we review compelling morphogenetic, cellular, and molecular studies that, taken together, suggest that there is an important window of opportunity during fetal life to positively impact brain development to improve postnatal neurocognition and behavior. Although substantial progress has been made in understanding the basic neurobiology of Down syndrome (DS), the majority of pre-clinical trials is currently focused on adults. There are a number of challenges in the identification and development of novel antenatal therapies for DS, including the lack of toxicity and teratogenicity for the pregnant woman and the fetus, evidence that the compounds can cross the placenta and achieve therapeutic levels, and the demonstration of clinical improvement. Preliminary experiments in mouse models suggest that prenatal treatment of DS is an achievable goal. © 2013 John Wiley & Sons, Ltd.     

Amazonian deforestation and soil biodiversity

Clearance and perturbation of Amazonian forests are one of the greatest threats to tropical biodiversity conservation of our times. A better understanding of how soil communities respond to Amazonian deforestation is crucially needed to inform policy interventions that effectively protect biodiversity and the essential ecosystem services it provides. We assessed the impact of deforestation and ecosystem conversion to arable land on Amazonian soil biodiversity through a meta-analysis. We analyzed 274 pairwise comparisons of soil biodiversity in Amazonian primary forests and sites under different stages of deforestation and land-use conversion: disturbed (wildfire and selective logging) and slash-and-burnt forests, pastures, and cropping systems. Overall, 60% and 51% of responses of soil macrofauna and microbial community attributes (i.e., abundance, biomass, richness, and diversity indexes) to deforestation were negative, respectively. We found few studies on mesofauna (e.g., microarthropods) and microfauna (e.g., protozoa and nematodes), so those groups could not be analyzed. Macrofauna abundance and biomass were more vulnerable to the displacement of forests by pastures than by agricultural fields, whereas microbes showed the opposite pattern. Effects of Amazonian deforestation on macrofauna were more detrimental at sites with mean annual precipitation >1900 mm, and higher losses of microbes occurred in highly acidic soils (pH < 4.5). Limited geographic coverage, omission of meso- and microfauna, and low taxonomic resolution were main factors impairing generalizations from the data set. Few studies assessed the impacts of within-forest disturbance (wildfires and selective logging) on soil species in Amazonia, where logging operations rapidly expand across public lands and more frequent severe dry seasons are increasing the prevalence of wildfires.     

Assessment of Flood Losses with Household Responses: Agent-Based Simulation in an Urban Catchment Area

Densely populated coastal urban areas are often exposed to multiple hazards, in particular floods and storms. Flood defenses and other engineering measures contribute to the mitigation of flood hazards, but a holistic approach to flood risk management should consider other interventions from the human side, including warning information, adaptive behavior, people/property evacuation, and the multilateral relief in local communities. There are few simulation approaches to consider these factors, and these typically focus on collective human actions. This paper presents an agent-based model that simulates flood response preferences and actions taken within individual households to reduce flood losses. The model implements a human response framework in which agents assess different flood scenarios according to warning information and decide whether and how much they invest in response measures to reduce potential inundation damages. A case study has been carried out in the Ng Tung River basin, an urbanized watershed in northern Hong Kong. Adopting a digital elevation model (DEM) as the modeling environment and a building map of household locations in the case area, the model considers the characteristics of households and the flood response behavior of their occupants. We found that property value, warning information, and storm conditions all influence household losses, with downstream and high density areas being particularly vulnerable. Results further indicate (i) that a flood warning system, which provides timely, accurate, and broad coverage rainstorm warning, can reduce flood losses by 30–40%; and (ii) to reduce losses, it is more effective and cheaper to invest early in response measures than late actions. This dynamic agent-based modeling approach is an innovative attempt to quantify and model the role of human responses in flood loss assessments. The model is demonstrated being useful for analyzing household scale flood losses and responses and it has the potential to contribute to flood emergency planning resource allocation in pluvial flood incidents.     

Global mismatches in aboveground and belowground biodiversity

Human activities are accelerating global biodiversity change and have resulted in severely threatened ecosystem services. A large proportion of terrestrial biodiversity is harbored by soil, but soil biodiversity has been omitted from many global biodiversity assessments and conservation actions, and understanding of global patterns of soil biodiversity remains limited. In particular, the extent to which hotspots and coldspots of aboveground and soil biodiversity overlap is not clear. We examined global patterns of these overlaps by mapping indices of aboveground (mammals, birds, amphibians, vascular plants) and soil (bacteria, fungi, macrofauna) biodiversity that we created using previously published data on species richness. Areas of mismatch between aboveground and soil biodiversity covered 27% of Earth's terrestrial surface. The temperate broadleaf and mixed forests biome had the highest proportion of grid cells with high aboveground biodiversity but low soil biodiversity, whereas the boreal and tundra biomes had intermediate soil biodiversity but low aboveground biodiversity. While more data on soil biodiversity are needed, both to cover geographic gaps and to include additional taxa, our results suggest that protecting aboveground biodiversity may not sufficiently reduce threats to soil biodiversity. Given the functional importance of soil biodiversity and the role of soils in human well-being, soil biodiversity should be considered further in policy agendas and conservation actions by adapting management practices to sustain soil biodiversity and considering soil biodiversity when designing protected areas.     

Application of a multi-plant QRA: A case study investigating the risk impact of the construction of a new plant on an existing chemical plant's risk levels

The construction of chemical clusters whereby a variety of chemical plants are located next to each other provides great economic benefits. However, in such clusters, due to the mere scale on which hazardous materials are processed, stored and handled, the potential of various accidents is much higher than in single companies. Furthermore, the close proximity of process installations and storage tanks in such areas gives rise to the risk of domino effects. Therefore, land use planning and layout design has always been a challenge within such clusters.In this paper, a Quantitative Risk Assessment (QRA) is carried out and used as a decision making tool to evaluate the acceptability of constructing a new chemical plant adjacent to an existing one. For this purpose, standard parameters such as individual risk and societal risk were quantified, before and after the new plant would come into operation. Given the experience of past accidents in the process industries, the likelihood of domino accidents in the two neighboring plants has also been analyzed.