Errors in device localization in MRI using Z-frames.

The use of a passive MRI-visible tracking frame is a common method of localizing devices in MRI space for MRI-guided procedures. One of the most common tracking frame designs found in the literature is the z-frame, as it allows six degree-of-freedom pose estimation using only a single image slice. Despite the popularity of this design, it is susceptible to errors in pose estimation due to various image distortion mechanisms in MRI. In this paper, the absolute error in using a z-frame to localize a tool in MRI is quantified over various positions of the z-frame relative to the MRI isocenter, and for various levels of static magnetic field inhomogeneity. It was found that the error increases rapidly with distance from the isocenter in both the horizontal and vertical directions, but the error is much less sensitive to position when multiple contiguous slices are used with slice-select gradient nonlinearity correction enabled, as opposed to the more common approach of only using a single image slice. In addition, the error is found to increase rapidly with an increasing level of static field inhomogeneity, even with the z-frame placed within 10 cm of the isocenter

Cryo-EM structure of the protein-conducting ERAD channel Hrd1 in complex with Hrd3.

Misfolded endoplasmic reticulum (ER) proteins are retro-translocated through the membrane into the cytosol, where they are poly-ubiquitinated, extracted from the ER membrane, and degraded by the proteasome1-4, a pathway termed ER-associated protein degradation (ERAD). Proteins with misfolded domains in the ER lumen or membrane are discarded through the ERAD-L and -M pathways, respectively. In S. cerevisiae, both pathways require the ubiquitin ligase Hrd1, a multi-spanning membrane protein with a cytosolic RING finger domain5,6. Hrd1 is the crucial membrane component for retro-translocation7,8, but whether it forms a protein-conducting channel is unclear. Here, we report a cryo-electron microscopy (cryo-EM) structure of S. cerevisiae Hrd1 in complex with its ER luminal binding partner Hrd3. Hrd1 forms a dimer within the membrane with one or two Hrd3 molecules associated at its luminal side. Each Hrd1 molecule has eight trans-membrane segments, five of which form an aqueous cavity extending from the cytosol almost to the ER lumen, while a segment of the neighboring Hrd1 molecule forms a lateral seal. The aqueous cavity and lateral gate are reminiscent of features in protein-conducting conduits that facilitate polypeptide movement in the opposite direction, that is, from the cytosol into or across membranes9-11. Our results suggest that Hrd1 forms a retro-translocation channel for the movement of misfolded polypeptides through the ER membrane

phenix.mr_rosetta: molecular replacement and model rebuilding with Phenix and Rosetta.

The combination of algorithms from the structure-modeling field with those of crystallographic structure determination can broaden the range of templates that are useful for structure determination by the method of molecular replacement. Automated tools in phenix.mr_rosetta simplify the application of these combined approaches by integrating Phenix crystallographic algorithms and Rosetta structure-modeling algorithms and by systematically generating and evaluating models with a combination of these methods. The phenix.mr_rosetta algorithms can be used to automatically determine challenging structures. The approaches used in phenix.mr_rosetta are described along with examples that show roles that structure-modeling can play in molecular replacement

Four small puzzles that Rosetta doesn't solve

A complete macromolecule modeling package must be able to solve the simplest structure prediction problems. Despite recent successes in high resolution structure modeling and design, the Rosetta software suite fares poorly on deceptively small protein and RNA puzzles, some as small as four residues. To illustrate these problems, this manuscript presents extensive Rosetta results for four well-defined test cases: the 20-residue mini-protein Trp cage, an even smaller disulfide-stabilized conotoxin, the reactive loop of a serine protease inhibitor, and a UUCG RNA tetraloop. In contrast to previous Rosetta studies, several lines of evidence indicate that conformational sampling is not the major bottleneck in modeling these small systems. Instead, approximations and omissions in the Rosetta all-atom energy function currently preclude discriminating experimentally observed conformations from de novo models at atomic resolution. These molecular "puzzles" should serve as useful model systems for developers wishing to make foundational improvements to this powerful modeling suite.Comment: Published in PLoS One as a manuscript for the RosettaCon 2010 Special Collectio

The Latent Class Structure of ADHD is stable across informants

Previous studies have looked at the structure of attention-deficit/ hyperactivity disorder (ADHD) using latent class analysis (LCA) of Child Behavior Checklist (CBCL) or Diagnostic and Statistical Manual of Mental Disorders (DSM) symptom structure. These studies have identified distinct classes of children with inattentive, hyperactive, or combined subtypes and have used these classes to refine genetic analyses. The objective of the current report is to determine if the latent class structure of ADHD subtypes is consistent across informant using the Conners' Rating Scales (CRS). LCA was applied to CRS forms from mother, father, and teacher reports of 1837, 1329 and 1048 latency aged Dutch twins, respectively. The optimal solution for boys was a 5-class solution for mothers, a 3-class solution for fathers, and a 4-class solution for teachers. For girls, a 4-class solution for mothers and a 3-class for fathers and teachers was optimal. Children placed into a class by one informant had markedly increased odds ratio of being placed into the same or similar class by the other informants. Results from LCA using Dutch twins with the CRS show stability across informants suggesting that more stable phenotypes may be accessible for genotyping using a multi-informant approach

Viral trans-factor independent replication of human papillomavirus genomes

<p>Abstract</p> <p>Background</p> <p>Papillomaviruses (PVs) establish a persistent infection in the proliferating basal cells of the epithelium. The viral genome is replicated and maintained as a low-copy nuclear plasmid in basal keratinocytes. Bovine and human papillomaviruses (BPV and HPV) are known to utilize two viral proteins; E1, a DNA helicase, and E2, a transcription factor, which have been considered essential for viral DNA replication. However, growing evidence suggests that E1 and E2 are not entirely essential for stable replication of HPV.</p> <p>Results</p> <p>Here we report that multiple HPV16 mutants, lacking either or both E1 and E2 open reading frame (ORFs) and the long control region (LCR), still support extrachromosomal replication. Our data clearly indicate that HPV16 has a mode of replication, independent of viral trans-factors, E1 and E2, which is achieved by origin activity located outside of the LCR.</p

Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL2P, to perform learning from numerical vectors. C-IL2P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy