Threshold Static Automated Perimetry of the Full Visual Field in Idiopathic Intracranial Hypertension.

Purpose: To characterize visual loss across the full visual field in idiopathic intracranial hypertension (IIH) patients with mild central visual loss. Methods: We tested the full visual field (50° nasal, 80° temporal, 30° superior, 45° inferior) of 1 eye of 39 IIH patients by using static perimetry (size V) with the Open Perimetry Interface. Participants met the Dandy criteria for IIH and had at least Frisén grade 1 papilledema with better than -5 dB mean deviation (MD) centrally. Two observers (MW and AS) evaluated the visual field defects, adjudicated any differences, and reviewed optical coherence tomography data. Results: We found a greater MD loss peripherally than centrally (central 26°). The median MD (and corresponding median absolute deviations) was -1.37 dB (1.61 dB) for the periphery and -0.77 dB (0.87 dB) for the central 26°, P < 0.001. There were about 30% more abnormal test locations identified in the periphery (P = 0.12), and the mean defect depth increased with eccentricity (P < 0.001). The most frequent defect found was a temporal wedge (23% of cases) in the periphery with another 23% that included this sector with inferior temporal loss. Although the presence of papilledema limited correlation, 55% of the temporal wedge defects had optical coherence tomography retinal nerve fiber layer deficits in the corresponding superonasal location. Other common visual field defects were inferonasal loss, superonasal loss, and superior and inferior arcuate defects. Seven patients (18%) had visual field defects in the periphery with normal central visual field testing. Conclusion: In IIH patients, we found substantial visual loss both outside 30° of the visual field and inside 30° with the depth of the defect increasing linearly with eccentricity. Temporal wedge defects were the most common visual field defect in the periphery. Static threshold perimetry of the full visual field appears to be clinically useful in IIH patients.

Pediatric idiopathic retroperitoneal fibrosis.

Retroperitoneal fibrosis (RPF) is a very rare disease that is even more rare in the pediatric population. Even less common are idiopathic pediatric cases of retroperitoneal fibrosis, with a majority of reported pediatric retroperitoneal fibrosis cases being associated with secondary etiologies. We present an 11-year-old Caucasian female that was diagnosed with idiopathic retroperitoneal fibrosis using magnetic resonance imaging (MRI) to work-up severe bilateral hydronephrosis that was identified with retroperitoneal ultrasound. Given the uncommon nature of this serious condition, we present this case to illustrate the importance for physicians to include retroperitoneal fibrosis in the differential diagnosis of a pediatric patient presenting with obstructive urinary findings and understand the utility of using MRI to diagnosis and monitor this disease.

ß-sheet topology prediction with high precision and recall for ß and mixed α/ß proteins.

The prediction of the correct ß-sheet topology for pure ß and mixed α/ß proteins is a critical intermediate step toward the three dimensional protein structure prediction. The predicted beta sheet topology provides distance constraints between sequentially separated residues, which reduces the three dimensional search space for a protein structure prediction algorithm. Here, we present a novel mixed integer linear optimization based framework for the prediction of ß-sheet topology in ß and mixed α/ß proteins. The objective is to maximize the total strand-to-strand contact potential of the protein. A large number of physical constraints are applied to provide biologically meaningful topology results. The formulation permits the creation of a rank-ordered list of preferred ß-sheet arrangements. Finally, the generated topologies are re-ranked using a fully atomistic approach involving torsion angle dynamics and clustering. For a large, non-redundant data set of 2102 ß and mixed α/ß proteins with at least 3 strands taken from the PDB, the proposed approach provides the top 5 solutions with average precision and recall greater than 78%. Consistent results are obtained in the ß-sheet topology prediction for blind targets provided during the CASP8 and CASP9 experiments, as well as for actual and predicted secondary structures. The ß-sheet topology prediction algorithm, BeST, is available to the scientific community at http://selene.princeton.edu/BeST/.

A novel framework for predicting in vivo toxicities from in vitro data using optimal methods for dense and sparse matrix reordering and logistic regression.

In this work, we combine the strengths of mixed-integer linear optimization (MILP) and logistic regression for predicting the in vivo toxicity of chemicals using only their measured in vitro assay data. The proposed approach utilizes a biclustering method based on iterative optimal reordering (DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., and Rabitz, H. A. (2008). Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies. BMC Bioinformatics 9, 458-474.; DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Rabinowitz, J. D., and Rabitz, H. A. (2010b). A network flow model for biclustering via optimal re-ordering of data matrices. J. Global. Optim. 47, 343-354.) to identify biclusters corresponding to subsets of chemicals that have similar responses over distinct subsets of the in vitro assays. The biclustering of the in vitro assays is shown to result in significant clustering based on assay target (e.g., cytochrome P450 [CYP] and nuclear receptors) and type (e.g., downregulated BioMAP and biochemical high-throughput screening protein kinase activity assays). An optimal method based on mixed-integer linear optimization for reordering sparse data matrices (DiMaggio, P. A., McAllister, S. R., Floudas, C. A., Feng, X. J., Li, G. Y., Rabinowitz, J. D., and Rabitz, H. A. (2010a). Enhancing molecular discovery using descriptor-free rearrangement clustering techniques for sparse data sets. AIChE J. 56, 405-418.; McAllister, S. R., DiMaggio, P. A., and Floudas, C. A. (2009). Mathematical modeling and efficient optimization methods for the distance-dependent rearrangement clustering problem. J. Global. Optim. 45, 111-129) is then applied to the in vivo data set (21.7% sparse) in order to cluster end points that have similar lowest effect level (LEL) values, where it is observed that the end points are effectively clustered according to (1) animal species (i.e., the chronic mouse and chronic rat end points were clearly separated) and (2) similar physiological attributes (i.e., liver- and reproductive-related end points were found to separately cluster together). As the liver and reproductive end points exhibited the largest degree of correlation, we further analyzed them using regularized logistic regression in a rank-and-drop framework to identify which subset of in vitro features could be utilized for in vivo toxicity prediction. It was observed that the in vivo end points that had similar LEL responses over the 309 chemicals (as determined by the sparse clustering results) also shared a significant subset of selected in vitro descriptors. Comparing the significant descriptors between the two different categories of end points revealed a specificity of the CYP assays for the liver end points and preferential selection of the estrogen/androgen nuclear receptors by the reproductive end points.

Selecting high quality protein structures from diverse conformational ensembles.

Protein structure prediction encompasses two major challenges: 1), the generation of a large ensemble of high resolution structures for a given amino-acid sequence; and 2), the identification of the structure closest to the native structure for a blind prediction. In this article, we address the second challenge, by proposing what is, to our knowledge, a novel iterative traveling-salesman problem-based clustering method to identify the structures of a protein, in a given ensemble, which are closest to the native structure. The method consists of an iterative procedure, which aims at eliminating clusters of structures at each iteration, which are unlikely to be of similar fold to the native, based on a statistical analysis of cluster density and average spherical radius. The method, denoted as ICON, has been tested on four data sets: 1), 1400 proteins with high resolution decoys; 2), medium-to-low resolution decoys from Decoys 'R' Us; 3), medium-to-low resolution decoys from the first-principles approach, ASTRO-FOLD; and 4), selected targets from CASP8. The extensive tests demonstrate that ICON can identify high-quality structures in each ensemble, regardless of the resolution of conformers. In a total of 1454 proteins, with an average of 1051 conformers per protein, the conformers selected by ICON are, on an average, in the top 3.5% of the conformers in the ensemble.