Active linearized PGC demodulation with fusion of PGC-Arctan and PGC-DCM schemes for nonlinear error elimination in SPM interferometer.

To eliminate the nonlinear error of phase generated carrier (PGC) demodulation in sinusoidal phase modulating interferometer (SPMI), an active linearized PGC demodulation with fusion of differential-and-cross-multiplying (PGC-DCM) and the arctangent (PGC-Arctan) schemes is proposed. In this method, the periodic integer multiple of π (π-integer phases) of PGC-Arctan without nonlinear error and the corresponding PGC-DCM results recorded at the same time are fused to obtain a calibration coefficient for PGC-DCM demodulation. Combining the accurate π-integer phases of PGC-Arctan and the calibrated fractional phase in the range of π of PGC-DCM, a linearized PGC demodulation result can be achieved, effectively eliminating the nonlinear error caused by drifts of phase demodulation depth (m) and carrier phase delay (θ). The distinct advantage of the proposed method is that it actively and linearly calibrates the fractional result of PGC-DCM without needing to measure or compensate m and θ. Simulation and displacement measurement experiments with different m and inherent arbitrary θ are performed to validate the proposed method. The experimental results show that nonlinear error of the proposed method can be reduced to about 0.1 nm with real-time linearization.

Expanding Structural Space for Immunomodulatory Nucleic Acid Nanoparticles (Nanps) via Spatial Arrangement of Their Therapeutic Moieties.

Different therapeutic nucleic acids (TNAs) can be unified in a single structure by their elongation with short oligonucleotides designed to self-assemble into nucleic acid nanoparticles (NANPs). With this approach, therapeutic cocktails with precisely controlled composition and stoichiometry of active ingredients can be delivered to the same diseased cells for enhancing pharmaceutical action. In this work, an additional nanotechnology-based therapeutic option that enlists a biocompatible NANP-encoded platform for their controlled patient-specific immunorecognition is explored. For this, a set of representative functional NANPs is extensively characterized in vitro, ex vivo, and in vivo and then further analyzed for immunostimulation of human peripheral blood mononuclear cells freshly collected from healthy donor volunteers. The results of the study present the advancement of the current TNA approach toward personalized medicine and offer a new strategy to potentially address top public health challenges related to drug overdose and safety through the biodegradable nature of the functional platform with immunostimulatory regulation.

Evaluation of Immunoprotective Effects of Fusobacterium necrophorum Outer Membrane Proteins 43K OMP, Leukotoxin and Hemolysin Multi-Component Recombinant Subunit Vaccine in Mice.

We evaluated the efficacy of three vaccine formulations containing different combinations of proteins (43K OMP, leukotoxin recombinant protein PL4 and hemolysin recombinant protein H2) and killed whole cell Fusobacterium necrophorum in preventing liver abscess. Four subcutaneous vaccines were formulated: vaccine 1 (43K OMP), vaccine 2 (PL4 and H2), vaccine 3 (43K OMP, PL4 and H2), and vaccine 4 (killed whole bacterial cell). 43K OMP, PL4, and H2 proteins were produced by using recombinant protein expression. To evaluate vaccine efficacy, we randomly allocated 50 BALB/c female mice to one of five different treatment groups: PBS control group, vaccine 1, vaccine 2, vaccine 3, and vaccine 4. Mice were vaccinated three times, with 14 days between each immunization. After immunization, the mice were challenged with F. necrophorum. The three key findings of this study are as follows: (1) Vaccine 3 has enabled mice to produce higher antibody titer following bacterial challenge, (2) in the liver pathology of mice, the vaccine 3 liver showed the least pathology, and (3) all four vaccines produced high levels of antibodies and cytokines in mice, but the level of vaccine 3 was the highest. Based on our results, it has been demonstrated that a mixture of F. necrophorum 43K OMP, PL4, and H2 proteins inoculated with mice can achieve protection against liver abscess in mice. Our research may therefore provide the basis for the development of a vaccine against F. necrophorum bovine infections.

Screening of BHK-21 cellular proteins that interact with outer membrane protein 43K OMP of Fusobacterium necrophorum.

Fusobacterium necrophorum is a Gram negative, spore-free, anaerobic bacterium that can cause pyogenic and necrotic infections in animals and humans. It is a major bovine pathogen and causes hepatic abscesses, foot rot, and necrotic laryngitis. The 43K OMP of F. necrophorum is an outer membrane protein with molecular weight of 43 kDa, exhibiting similarity to pore-forming proteins of other Fusobacterium species that plays an important role in bacterial infections. However, the role of 43K OMP in F. necrophorum adhesion remains unknown. In this study, we evaluated whether the 43K OMP of F. necrophorum mediates adhesion to BHK-21 cells and performed a preliminary screen of the proteins that interact with 43K OMP of F. necrophorum by immunoprecipitation-mass spectrometry. The results showed that the natural 43K OMP and recombinant 43K OMP could bind to BHK-21 cells, and preincubation of F. necrophorum with an antibody against the recombinant 43K OMP of F. necrophorum decreased binding to BHK-21 cells. Seventy differential interacting proteins were successfully screened by immunoprecipitation-mass spectrometry. Among these seventy differential interacting proteins, seven cell membrane proteins and four extracellular matrix proteins shown to be relevant to bacteria adhesion through subcellular localization and single-molecule function analysis. These data increase our understanding of the pathogenesis of F. necrophorum and provide a new theoretical basis for the design of antimicrobial drugs against F. necrophorum.

Manganese Chloride Exposure Causes Disorder of Energy Metabolism and Induces Oxidative Stress and Autophagy in Chicken Liver.

Manganese (Mn) pollution is an important environmental problem because of the potential toxicity to human and animal health. However, the effects of Mn on energy metabolism and autophagy are not clear. Consequently, we examined the effects of excessive and chronic exposure to Mn on liver function, oxidative stress, respiratory chain complex activity, and autophagy in chicken liver. Our results indicated that the accumulation of Mn in the liver and levels of AST and ALT in the serum of the Mn-exposed group were significantly higher (P < 0.05) than those in the control group at 90 days; the activities of GSH-Px, SOD, CAT, Na+-K+-ATPase, Mg2+-ATPase, Ca2+-ATPase, and respiratory chain complexes (I, II, III) in the Mn-exposed group were significantly decreased (P < 0.05) compared to the control group. However, the MDA content, NO content, iNOS activity, mRNA and protein levels of iNOS, and autophagy-related genes in the Mn-exposed group were significantly increased (P < 0.05) compared to the control group. In contrast, the mRNA level and protein expression of mTOR were significantly decreased (P < 0.05) compared to the control group. Furthermore, the characteristic autophagic vacuolar organelles were observed in the Mn-exposed group. These results suggested that excess Mn exposure can cause a disorder of energy metabolism by mitochondrial injury and induce oxidative stress and autophagy, which eventually lead to liver damage.

Risk-adjusted monitoring of surgical performance.

We propose a nonparametric risk-adjusted cumulative sum chart to monitor surgical outcomes for patients with different risks of post-operative mortality due to risk factors that exist before the surgery. Using varying-coefficient logistic regression models, we accomplish the risk adjustment. Unknown coefficient functions are estimated by global polynomial spline approximation based on the maximum likelihood principle. We suggest a bisection minimization approach and a bootstrap method to determine the chart testing limit value. Compared with the previous (parametric) risk-adjusted cumulative sum chart, a major advantage of our method is that the morality rate can be modeled more flexibly by related covariates, which significantly enhances the monitoring efficiency. Simulations demonstrate nice performance of our proposed procedure. An application to a UK cardiac surgery dataset illustrates the use of our methodology.

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.

Failure time regression with continuous informative auxiliary covariates.

In this paper we use Cox's regression model to fit failure time data with continuous informative auxiliary variables in the presence of a validation subsample. We first estimate the induced relative risk function by kernel smoothing based on the validation subsample, and then improve the estimation by utilizing the information on the incomplete observations from non-validation subsample and the auxiliary observations from the primary sample. Asymptotic normality of the proposed estimator is derived. The proposed method allows one to robustly model the failure time data with an informative multivariate auxiliary covariate. Comparison of the proposed approach with several existing methods is made via simulations. Two real datasets are analyzed to illustrate the proposed method.

REGULARIZATION FOR COX'S PROPORTIONAL HAZARDS MODEL WITH NP-DIMENSIONALITY.

High throughput genetic sequencing arrays with thousands of measurements per sample and a great amount of related censored clinical data have increased demanding need for better measurement specific model selection. In this paper we establish strong oracle properties of non-concave penalized methods for non-polynomial (NP) dimensional data with censoring in the framework of Cox's proportional hazards model. A class of folded-concave penalties are employed and both LASSO and SCAD are discussed specifically. We unveil the question under which dimensionality and correlation restrictions can an oracle estimator be constructed and grasped. It is demonstrated that non-concave penalties lead to significant reduction of the "irrepresentable condition" needed for LASSO model selection consistency. The large deviation result for martingales, bearing interests of its own, is developed for characterizing the strong oracle property. Moreover, the non-concave regularized estimator, is shown to achieve asymptotically the information bound of the oracle estimator. A coordinate-wise algorithm is developed for finding the grid of solution paths for penalized hazard regression problems, and its performance is evaluated on simulated and gene association study examples.