PURPOSE: This paper describes an approach for the three-dimensional (3D) shape and pose reconstruction of the human rib cage from few segmented two-dimensional (2D) projection images. Our work is aimed at supporting temporal subtraction techniques of subsequently acquired radiographs by establishing a method for the assessment of pose differences in sequences of chest radiographs of the same patient.

METHODS: The reconstruction method is based on a 3D statistical shape model (SSM) of the rib cage, which is adapted to binary 2D projection images of an individual rib cage. To drive the adaptation we minimize a distance measure that quantifies the dissimilarities between 2D projections of the 3D SSM and the projection images of the individual rib cage. We propose different silhouette-based distance measures and evaluate their suitability for the adaptation of the SSM to the projection images.

RESULTS: An evaluation was performed on 29 sets of biplanar binary images (posterior-anterior and lateral). Depending on the chosen distance measure, our experiments on the combined reconstruction of shape and pose of the rib cages yield reconstruction errors from 2.2 to 4.7 mm average mean 3D surface distance. Given a geometry of an individual rib cage, the rotational errors for the pose reconstruction range from 0.1 degrees to 0.9 degrees.

CONCLUSIONS: The results show that our method is suitable for the estimation of pose differences of the human rib cage in binary projection images. Thus, it is able to provide crucial 3D information for registration during the generation of 2D subtraction images.

The dynamic evolution of organelle compartmentalization in eukaryotes and how strictly compartmentalization is maintained are matters of ongoing debate. While the endoplasmic reticulum (ER) is classically envisioned as the site of protein cotranslational translocation, it has recently been proposed to have pluripotent functions. Using transfected reporter constructs, organelle-specific markers, and functional enzyme assays, we now show that in an early-diverging protozoan, Giardia lamblia, endocytosis and subsequent degradation of exogenous proteins occur in the ER or in an adjacent and communicating compartment. The Giardia endomembrane system is simple compared to those of typical eukaryotes. It lacks peroxisomes, a classical Golgi apparatus, and canonical lysosomes. Giardia orthologues of mammalian lysosomal proteases function within an ER-like tubulovesicular compartment, which itself can dynamically communicate with clathrin-containing vacuoles at the periphery of the cell to receive endocytosed proteins. These primitive characteristics support Giardia's proposed early branching and could serve as a model to study the compartmentalization of endocytic and lysosomal functions into organelles distinct from the ER. This system also may have functional similarity to the retrograde transport of toxins and major histocompatibility complex class I function in the ER of mammals.

The efficient Suzuki cross-coupling of pyrazoline nonaflates with organoboron reagents was achieved to afford diverse 3-substituted-2-pyrazolines in excellent yield. The nonaflates displayed improved reactivity over the corresponding triflates and smoothly coupled to a variety of aryl- and heteroarylboronic acids. This process and its broad scope constitute a rapid, divergent strategy for the synthesis of elaborated 2-pyrazolines that are not readily obtained via conventional methods.

Gene expression depends upon the antagonistic actions of chromatin remodeling complexes. While this has been studied extensively for the enzymes that covalently modify the tails of histones, the mechanism of how ATP-dependent remodeling complexes antagonize each other to maintain the proper level of gene activity is not known. The gene encoding a large subunit of ribonucleotide reductase, RNR3, is regulated by ISW2 and SWI/SNF, complexes that repress and activate transcription, respectively. Here, we studied the functional interactions of these two complexes at RNR3. Deletion of ISW2 causes constitutive recruitment of SWI/SNF, and conditional reexpression of ISW2 causes the repositioning of nucleosomes and reduced SWI/SNF occupancy at RNR3. Thus, ISW2 is required for restriction of access of SWI/SNF to the RNR3 promoter under the uninduced condition. Interestingly, the binding of sequence-specific DNA binding factors and the general transcription machinery are unaffected by the status of ISW2, suggesting that disruption of nucleosome positioning does not cause a nonspecific increase in cross-linking of all factors to RNR3. We provide evidence that ISW2 does not act on SWI/SNF directly but excludes its occupancy by positioning nucleosomes over the promoter. Genetic disruption of nucleosome positioning by other means led to a similar phenotype, linking repressed chromatin structure to SWI/SNF exclusion. Thus, incorporation of promoters into a repressive chromatin structure is essential for prevention of the opportunistic actions of nucleosome-disrupting activities in vivo, providing a novel mechanism for maintaining tight control of gene expression.

Realistic computational models of single neurons require component ion channels that reproduce experimental findings. Here, a topology-mutating genetic algorithm that searches for the best state diagram and transition-rate parameters to model macroscopic ion-channel behavior is described. Important features of the algorithm include a topology-altering strategy, automatic satisfaction of equilibrium constraints (microscopic reversibility), and multiple-protocol fitting using sequential goal programming rather than explicit weighting. Application of this genetic algorithm to design a sodium-channel model exhibiting both fast and prolonged inactivation yields a six-state model that produces realistic activity-dependent attenuation of action-potential backpropagation in current-clamp simulations of a CA1 pyramidal neuron.

Amyloid fibrils characterize a diverse group of human diseases that includes Alzheimer’s disease, Creutzfeldt-Jakob and type II diabetes. Alzheimer’s amyloid fibrils consist of amyloid-beta (Abeta) peptide and occur in a range of structurally different fibril morphologies. The structural characteristics of 12 single Abeta(1-40) amyloid fibrils, all formed under the same solution conditions, were determined by electron cryo-microscopy and three-dimensional reconstruction. The majority of analyzed fibrils form a range of morphologies that show almost continuously altering structural properties. The observed fibril polymorphism implies that amyloid formation can lead, for the same polypeptide sequence, to many different patterns of inter- or intra-residue interactions. This property differs significantly from native, monomeric protein folding reactions that produce, for one protein sequence, only one ordered conformation and only one set of inter-residue interactions.

We introduce an efficient search strategy to substantially accelerate feature based registration. Previous feature based registration algorithms often use truncated search strategies in order to achieve small computation times. Our new accelerated search strategy is based on the realization that the search for corresponding features can be dramatically accelerated by utilizing Johnson-Lindenstrauss dimension reduction. Order of magnitude calculations for the search strategy we propose here indicate that the algorithm proposed is more than a million times faster than previously utilized naive search strategies, and this advantage in speed is directly translated into an advantage in accuracy as the fast speed enables more comparisons to be made in the same amount of time. We describe the accelerated scheme together with a full complexity analysis. The registration algorithm was applied to large transmission electron microscopy (TEM) images of neural ultrastructure. Our experiments demonstrate that our algorithm enables alignment of TEM images with increased accuracy and efficiency compared to previous algorithms.

For the analysis of neuronal networks it is an important yet unresolved task to relate the neurons’ activities to their morphology. Here we introduce activity correlation imaging to simultaneously visualize the activity and morphology of populations of neurons. To this end we first stain the network’s neurons using a membrane-permeable [Ca(2+)] indicator (e.g., Fluo-4/AM) and record their activities. We then exploit the recorded temporal activity patterns as a means of intrinsic contrast to visualize individual neurons’ dendritic morphology. The result is a high-contrast, multicolor visualization of the neuronal network. Taking the Xenopus olfactory bulb as an example we show the activities of the mitral/tufted cells of the olfactory bulb as well as their projections into the olfactory glomeruli. This method, yielding both functional and structural information of neuronal populations, will open up unprecedented possibilities for the investigation of neuronal networks.

Camera networks are widely used for tasks such as surveillance, monitoring and tracking. In order to accomplish these tasks, knowledge of localization information such as camera locations and other geometric constraints about the environment (e.g. walls, rooms, and building layout) are typically considered to be essential. However, this information is not always required for many tasks such as estimating the topology of camera network coverage, or coordinate-free object tracking and navigation. In this paper, we propose a simplicial representation (called CN- complex) that can be constructed from discrete local observations from cameras, and utilize this novel representation to recover the topological information of the network coverage. We prove that our representation captures the correct topological information from network coverage for 2.5-D layouts, and demonstrate their utility in simulations as well as a real-world experimental set-up. Our proposed approach is particularly useful in the context of ad-hoc camera networks in indoor/outdoor urban environments with distributed but limited computational power and energy.

In this paper we propose a framework for fully automatic, robust and accurate segmentation of the human pelvis and proximal femur in CT data. We propose a composite statistical shape model of femur and pelvis with a flexible hip joint, for which we extend the common definition of statistical shape models as well as the common strategy for their adaptation. We do not analyze the joint flexibility statistically, but model it explicitly by rotational parameters describing the bent in a ball-and-socket joint. A leave-one-out evaluation on 50 CT volumes shows that image driven adaptation of our composite shape model robustly produces accurate segmentations of both proximal femur and pelvis. As a second contribution, we evaluate a fine grain multi-object segmentation method based on graph optimization. It relies on accurate initializations of femur and pelvis, which our composite shape model can generate. Simultaneous optimization of both femur and pelvis yields more accurate results than separate optimizations of each structure. Shape model adaptation and graph based optimization are embedded in a fully automatic framework.