Registration of Brain MRI/PET Images Based on Adaptive Combination of Intensity and Gradient Field Mutual Information

Traditional mutual information (MI) function aligns two multimodality images with intensity information, lacking spatial information, so that it usually presents many local maxima that can lead to inaccurate registration. Our paper proposes an algorithm of adaptive combination of intensity and gradient field mutual information (ACMI). Gradient code maps (GCM) are constructed by coding gradient field information of corresponding original images. The gradient field MI, calculated from GCMs, can provide complementary properties to intensity MI. ACMI combines intensity MI and gradient field MI with a nonlinear weight function, which can automatically adjust the proportion between two types MI in combination to improve registration. Experimental results demonstrate that ACMI outperforms the traditional MI and it is much less sensitive to reduced resolution or overlap of images

Editorial: Polymer Solar Cells: Molecular Design and Microstructure Control

[No abstract available

MOLECULAR PROFILING IN BREAST CANCER AND TOXICOGENOMICS

Indiana University-Purdue University Indianapolis (IUPUI)This dissertation presents a body of research that attempts to tackle the ‘overfitting’ problem for gene signature and biomarker development in two different aspects (mechanistically and computationally). In achievement of a deeper understanding of cancer molecular mechanisms, this study presents new approaches to derive gene signatures for various biological phenotypes, including breast cancer, in the context of well-defined and mechanistically associated biological pathways. We identified the pattern of gene expression in the cell cycle pathway can indeed serve as a powerful biomarker for breast cancer prognosis. We further built a predictive model for prognosis based on the cell cycle gene signature, and found our model to be more accurate than the Amsterdam 70-gene signature when tested with multiple gene expression datasets generated from several patient populations. Aside from demonstrating the effectiveness of dimensionality reduction, phenotypic dissection, and prognostic or diagnostic prediction, this approach also provides an alternative to the current methodology of identifying gene expression markers that links to biological mechanism. This dissertation also presents the development of a novel feature selection algorithm called Predictive Power Estimate Analysis (PPEA) to computationally tackle on overfitting. The algorithm iteratively apply a two-way bootstrapping procedure to estimate predictive power of each individual gene, and make it possible to construct a predictive model from a much smaller set of genes with the highest predictive power. Using DrugMatrix™ rat liver data, we identified genomic biomarkers of hepatic specific injury for inflammation, cell death, and bile duct hyperplasia. We demonstrated that the signature genes were mechanistically related to the phenotype the signature intended to predict (e.g. 17 out of top 20 genes for inflammation selected by PPEA were members of NF-kB pathway, which is a key pre-inflammatory pathway for a xenobiotic response). The top 4 gene signature for BDH has been further validated by QPCR in a toxicology lab. This is important because our results suggest that the PPEA model not largely deters the over-fitting problem, but also has the capability to elucidate mechanism(s) of drug action and / or of toxicity

Mitochondrial nutrients improve immune dysfunction in the type 2 diabetic Goto-Kakizaki rats.

The development of type 2 diabetes is accompanied by decreased immune function and the mechanisms are unclear. We hypothesize that oxidative damage and mitochondrial dysfunction may play an important role in the immune dysfunction in diabetes. In the present study, we investigated this hypothesis in diabetic Goto-Kakizaki rats by treatment with a combination of four mitochondrial-targeting nutrients, namely, R-alpha-lipoic acid, acetyl-L-carnitine, nicotinamide and biotin. We first studied the effects of the combination of these four nutrients on immune function by examining cell proliferation in immune organs (spleen and thymus) and immunomodulating factors in the plasma. We then examined, in the plasma and thymus, oxidative damage biomarkers, including lipid peroxidation, protein oxidation, reactive oxygen species, calcium and antioxidant defence systems, mitochondrial potential and apoptosis-inducing factors (caspase 3, p53 and p21). We found that immune dysfunction in these animals is associated with increased oxidative damage and mitochondrial dysfunction and that the nutrient treatment effectively elevated immune function, decreased oxidative damage, enhanced mitochondrial function and inhibited the elevation of apoptosis factors. These effects are comparable to, or greater than, those of the anti-diabetic drug pioglitazone. These data suggest that a rational combination of mitochondrial-targeting nutrients may be effective in improving immune function in type 2 diabetes through enhancement of mitochondrial function, decreased oxidative damage, and delayed cell death in the immune organs and blood

S-model speed planning of NURBS curve based on uniaxial performance limitation

As more complex curves are used in current productions, curve speed planning has become a key technique to overcome the bottleneck of high-speed and high-precision computerized numerical control (CNC) systems. This paper first establishes the model of particle velocity, acceleration and jerk in Cartesian coordinate system, and then improves the uniaxial performance limit algorithm.We set up a real-time speed planning process of spline curves, design a S-model look-ahead algorithm, simplify the S-model speed planning algorithm, and achieve real-time non-uniform rational B-splines (NUBRS) curve S-model speed planning based on uniaxial performance limitation. Simulation results show that all the actual interpolation velocity, acceleration and jerk under the proposed method meet the preset single-axis limit. Experimental results show that the tracking performance under the proposed method has been significantly improved compared with that under the small line segments method. Compared with the NUBRS curve trapezoidal model speed planning, the vibration spikes during machining can be eliminated

Intrinsic Disorder in Transcription Factors

Submitted to the faculty of the Indiana University School of Informatics Graduate School in partial fulfillment of the requirements for the degree Master of Sciences in Bioinformatics, August 2005Reported evidence suggested that high abundance of intrinsic disorder in eukaryotic genomes in comparison to bacteria and archaea may reflect the greater need for disorder-associated signaling and transcriptional regulation in nucleated cells. The major advantage of intrinsically disordered proteins or disordered regions is their inherent plasticity for molecular recognition, and this advantage promotes disordered proteins or disordered regions in binding their targets with high specificity and low affinity and with numerous partners. Although several well-characterized examples of intrinsically disordered proteins in transcriptional regulation have been reported and the biological functions associated with their corresponding structural properties have been examined, so far no specific systematic analysis of intrinsically disordered proteins has been reported. To test for a generalized prevalence of intrinsic disorder in transcriptional regulation, we first used the Predictor Of Natural Disorder Regions (PONDR VL-XT) to systematically analyze the intrinsic disorder in three Transcription Factor (TF) datasets (TFSPTRENR25, TFSPNR25, TFNR25) and two control sets (PDBs25 and RandomACNR25). PONDR VL-XT predicts regions of ≥30 consecutive disordered residues for 94.13%, 85.19%, 82.63%, 54.51%, and 18.64% of the proteins from TFNR25, TFSPNR25, TFSPTRENR25, RandomACNR25, and PDBs25, respectively, indicating significant abundance of intrinsic disorder in TFs as compared to the two control sets. We then used Cumulative Distribution Function (CDF) and charge-hydropathy plots to further confirm this propensity for intrinsic disorder in TFs. The amino acid compositions results showed that the three TF datasets differed significantly 5 from the two control sets. All three TF datasets were substantially depleted in order-promoting residues such as W, F, I, Y, and V, and significantly enriched in disorder-promoting residues such as Q, S, and P. H and C were highly over-represented in TF datasets because nearly a half of TFs contain several zinc-fingers and the most popular type of zinc-finger is C2H2. High occurrence of proline and glutamine in these TF datasets suggests that these residues might contribute to conformational flexibility needed during the process of binding by co-activators or repressors during transcriptional activation or repression. The data for disorder predictions on TF domains showed that the AT-hooks and basic regions of DNA Binding Domains (DBDs) were highly disordered (the overall disorder scores are 99% and 96% respectively). The C2H2 zinc-fingers were predicted to be highly ordered; however, the longer the zinc finger linkers, the higher the predicted magnitude of disorder. Overall, the degree of disorder in TF activation regions was much higher than that in DBDs. Our studies also confirmed that the degree of disorder was significantly higher in eukaryotic TFs than in prokaryotic TFs, and the results reflected the fact that the eukaryotes have well-developed elaborated gene transcription mechanism, and such a system is in great need of TF flexibility. Taken together, our data suggests that intrinsically disordered TFs or partially unstructured regions in TFs play key roles in transcriptional regulation, where folding coupled to binding is a common mechanism