Phylogenetic approach reveals that virus genotype largely determines HIV set-point viral load

HIV virulence, i.e. the time of progression to AIDS, varies greatly among patients. As for other rapidly evolving pathogens of humans, it is difficult to know if this variance is controlled by the genotype of the host or that of the virus because the transmission chain is usually unknown. We apply the phylogenetic comparative approach (PCA) to estimate the heritability of a trait from one infection to the next, which indicates the control of the virus genotype over this trait. The idea is to use viral RNA sequences obtained from patients infected by HIV-1 subtype B to build a phylogeny, which approximately reflects the transmission chain. Heritability is measured statistically as the propensity for patients close in the phylogeny to exhibit similar infection trait values. The approach reveals that up to half of the variance in set-point viral load, a trait associated with virulence, can be heritable. Our estimate is significant and robust to noise in the phylogeny. We also check for the consistency of our approach by showing that a trait related to drug resistance is almost entirely heritable. Finally, we show the importance of taking into account the transmission chain when estimating correlations between infection traits. The fact that HIV virulence is, at least partially, heritable from one infection to the next has clinical and epidemiological implications. The difference between earlier studies and ours comes from the quality of our dataset and from the power of the PCA, which can be applied to large datasets and accounts for within-host evolution. The PCA opens new perspectives for approaches linking clinical data and evolutionary biology because it can be extended to study other traits or other infectious diseases.

Kernel density correlation based non-rigid point set matching for statistical model-based 2D/3D reconstruction

This paper presents a kernel density correlation based nonrigid point set matching method and shows its application in statistical model based 2D/3D reconstruction of a scaled, patient-specific model from an un-calibrated x-ray radiograph. In this method, both the reference point set and the floating point set are first represented using kernel density estimates. A correlation measure between these two kernel density estimates is then optimized to find a displacement field such that the floating point set is moved to the reference point set. Regularizations based on the overall deformation energy and the motion smoothness energy are used to constraint the displacement field for a robust point set matching. Incorporating this non-rigid point set matching method into a statistical model based 2D/3D reconstruction framework, we can reconstruct a scaled, patient-specific model from noisy edge points that are extracted directly from the x-ray radiograph by an edge detector. Our experiment conducted on datasets of two patients and six cadavers demonstrates a mean reconstruction error of 1.9 mm

Deforestation and forest degradation monitoring and assessment of biomass and carbon stock of lowland rainforest in the Analanjirofo region, Madagascar

Madagascar is currently developing a policy and strategies to enhance the sustainable management of its natural resources, encouraged by United Nations Framework Convention on Climate Change (UNFCCC) and REDD. To set up a sustainable financing scheme methodologies have to be provided that estimate, prevent and mitigate leakage, develop national and regional baselines, and estimate carbon benefits. With this research study this challenge was tried to be addressed by analysing a lowland rainforest in the Analanjirofo region in the district of Soanierana Ivongo, North East of Madagascar. For two distinguished forest degradation stages: “low degraded forest” and “degraded forest” aboveground biomass and carbon stock was assessed. The corresponding rates of carbon within those two classes were calculated and linked to a multi-temporal set of SPOT satellite data acquired in 1991, 2004 and 2009. Deforestation and particularly degradation and the related carbon stock developments were analysed. With the assessed data for the 3 years 1991, 2004 and 2009 it was possible to model a baseline and to develop a forest prediction for 2020 for Analanjirofo region in the district of Soanierana Ivongo. These results, developed applying robust methods, may provide important spatial information regarding the priorities in planning and implementation of future REDD+ activities in the area.