Accurate and robust reconstruction of proximal femur from sparse intraoperative data and dense point distribution model for surgical navigation

Constructing a 3D surface model from sparse-point data is a nontrivial task. Here, we report an accurate and robust approach for reconstructing a surface model of the proximal femur from sparse-point data and a dense-point distribution model (DPDM). The problem is formulated as a three-stage optimal estimation process. The first stage, affine registration, is to iteratively estimate a scale and a rigid transformation between the mean surface model of the DPDM and the sparse input points. The estimation results of the first stage are used to establish point correspondences for the second stage, statistical instantiation, which stably instantiates a surface model from the DPDM using a statistical approach. This surface model is then fed to the third stage, kernel-based deformation, which further refines the surface model. Handling outliers is achieved by consistently employing the least trimmed squares (LTS) approach with a roughly estimated outlier rate in all three stages. If an optimal value of the outlier rate is preferred, we propose a hypothesis testing procedure to automatically estimate it. We present here our validations using four experiments, which include 1 leave-one-out experiment, 2 experiment on evaluating the present approach for handling pathology, 3 experiment on evaluating the present approach for handling outliers, and 4 experiment on reconstructing surface models of seven dry cadaver femurs using clinically relevant data without noise and with noise added. Our validation results demonstrate the robust performance of the present approach in handling outliers, pathology, and noise. An average 95-percentile error of 1.7-2.3 mm was found when the present approach was used to reconstruct surface models of the cadaver femurs from sparse-point data with noise added.

The double-stranded RNA-activated protein kinase mediates radiation resistance in mouse embryo fibroblasts through nuclear factor kappaB and Akt activation

PURPOSE: Activation of the double-stranded RNA-activated protein kinase (PKR) leads to the induction of various pathways including the down-regulation of translation through phosphorylation of the eukaryotic translation initiation factor 2alpha (eIF-2alpha). There have been no reports to date about the role of PKR in radiation sensitivity. EXPERIMENTAL DESIGN: A clonogenic survival assay was used to investigate the sensitivity of PKR mouse embryo fibroblasts (MEF) to radiation therapy. 2-Aminopurine (2-AP), a chemical inhibitor of PKR, was used to inhibit PKR activation. Nuclear factor-kappaB (NF-kappaB) activation was assessed by electrophoretic mobility shift assay (EMSA). Expression of PKR and downstream targets was examined by Western blot analysis and immunofluorescence. RESULTS: Ionizing radiation leads to dose- and time-dependent increases in PKR expression and function that contributes to increased cellular radiation resistance as shown by clonogenic survival and terminal nucleotidyl transferase-mediated nick end labeling (TUNEL) apoptosis assays. Specific inhibition of PKR with the chemical inhibitor 2-AP restores radiation sensitivity. Plasmid transfection of the PKR wild-type (wt) gene into PKR(-/-) MEFs leads to increased radiation resistance. The protective effect of PKR to radiation may be mediated in part through NF-kappaB and Akt because both NF-kappaB and Akt are activated after ionizing radiation in PKR+/+ but not PKR-/- cells. CONCLUSIONS: We suggest a novel role for PKR as a mediator of radiation resistance modulated in part through the protective effects of NF-kappaB and Akt activation. The modification of PKR activity may be a novel strategy in the future to overcome radiation resistance.

How stable are 20th century calibration models? A high-resolution summer temperature reconstruction for the eastern Swiss Alps back to A.D. 1580 derived from proglacial varved sediments

We found a significant positive correlation between local summer air temperature (May-September) and the annual sediment mass accumulation rate (MAR) in Lake Silvaplana (46°N, 9°E, 1800 m a.s.l.) during the twentieth century (r = 0.69, p < 0.001 for decadal smoothed series). Sediment trap data (2001-2005) confirm this relation with exceptionally high particle yields during the hottest summer of the last 140 years in 2003. On this base we developed a decadal-scale summer temperature reconstruction back to AD 1580. Surprisingly, the comparison of our reconstruction with two other independent regional summer temperature reconstructions (based on tree-rings and documentary data) revealed a significant negative correlation for the pre-1900 data (ie, late ‘Little Ice Age’). This demonstrates that the correlation between MAR and summer temperature is not stable in time and the actualistic principle does not apply in this case. We suggest that different climatic regimes (modern/‘Little Ice Age’) lead to changing state conditions in the catchment and thus to considerably different sediment transport mechanisms. Therefore, we calibrated our MAR data with gridded early instrumental temperature series from AD 1760-1880 (r = -0.48, p < 0.01 for decadal smoothed series) to properly reconstruct the late LIA climatic conditions. We found exceptionally low temperatures between AD 1580 and 1610 (0.75°C below twentieth-century mean) and during the late Maunder Minimum from AD 1680 to 1710 (0.5°C below twentieth-century mean). In general, summer temperatures did not experience major negative departures from the twentieth-century mean during the late ‘Little Ice Age’. This compares well with the two existing independent regional reconstructions suggesting that the LIA in the Alps was mainly a phenomenon of the cold season.