Bayesian Signal Reconstruction from Fourier Transform Magnitude in the Presence of Symmetries and X-ray Crystallography

In Ref. [I] a signal reconstruction problem motivated by x-ray crystallography was solved using a Bayesian statistical approach. The signal is zero-one, periodic, and substantial statistical a priori information is known, which is modeled with a Markov random field. The data are inaccurate magnitudes of the Fourier coefficients of the signal. The solution is explicit and the computational burden is independent of the signal dimension. In Ref, [2] a detailed parameterization of the a priori model appropriate for crystallography was proposed and symmetry-breaking parameters in the riolution were usecl to perform data-dependent adaptation of the estimator. The adaptation attempts to minimize the effects of the spherical model approximation used in the solution. In this paper these ideas are extended to signals that obey a space group syrrlmetry, which is a crucial extension for the x-ray crystallography application. Performance statistics for reconstruction in the presence of a space group symmetry based on simulated data are presented. [I.] Peter C. Doerschuk. Bayesian Signal Reconstruction, Markov Random Fields, and X-Ray Crystallography. Journal of the Optical Society of America A, 8(8):1207-1221, 1991. [2] Peter C. Doerschuk. Adaptive Bayesian Signal Reconstruction with A. Priori Model Implementation \u27and Synthetic Examples for X-ray Crystallography. Jounal of the Optical Society of America A, 8(8):1222-1232,1991

Learning Compositional Visual Concepts with Mutual Consistency

Compositionality of semantic concepts in image synthesis and analysis is appealing as it can help in decomposing known and generatively recomposing unknown data. For instance, we may learn concepts of changing illumination, geometry or albedo of a scene, and try to recombine them to generate physically meaningful, but unseen data for training and testing. In practice however we often do not have samples from the joint concept space available: We may have data on illumination change in one data set and on geometric change in another one without complete overlap. We pose the following question: How can we learn two or more concepts jointly from different data sets with mutual consistency where we do not have samples from the full joint space? We present a novel answer in this paper based on cyclic consistency over multiple concepts, represented individually by generative adversarial networks (GANs). Our method, ConceptGAN, can be understood as a drop in for data augmentation to improve resilience for real world applications. Qualitative and quantitative evaluations demonstrate its efficacy in generating semantically meaningful images, as well as one shot face verification as an example application.Comment: 10 pages, 8 figures, 4 tables, CVPR 201

Staphylococcus aureus α-Hemolysin Mediates Virulence in a Murine Model of Severe Pneumonia Through Activation of the NLRP3 Inflammasome

Staphylococcus aureus is a dangerous pathogen that can cause necrotizing infections characterized by massive inflammatory responses and tissue destruction. Staphylococcal α-hemolysin is an essential virulence factor in severe S. aureus pneumonia. It activates the nucleotide-binding domain and leucine-rich repeat containing gene family, pyrin domain containing 3 (NLRP3) inflammasome to induce production of interleukin-1β and programmed necrotic cell death. We sought to determine the role of α-hemolysin–mediated activation of NLRP3 in the pathogenesis of S. aureus pneumonia. We show that α-hemolysin activates the NLRP3 inflammasome during S. aureus pneumonia, inducing necrotic pulmonary injury. Moreover, Nlrp3−/− mice have less-severe pneumonia. Pulmonary injury induced by isolated α-hemolysin or live S. aureus is independent of interleukin-1β signaling, implicating NLRP3-induced necrosis in the pathogenesis of severe infection. This work demonstrates the exploitation of host inflammatory signaling by S. aureus and suggests the NLRP3 inflammasome as a potential target for pharmacologic interventions in severe S. aureus infections

Self-assembly of highly symmetrical, ultrasmall inorganic cages directed by surfactant micelles

Nanometre-sized objects with highly symmetrical, cage-like polyhedral shapes, often with icosahedral symmetry, have recently been assembled from DNA(1-3), RNA(4) or proteins(5,6) for applications in biology and medicine. These achievements relied on advances in the development of programmable self-assembling biological materials(7-10), and on rapidly developing techniques for generating three-dimensional (3D) reconstructions from cryo-electron microscopy images of single particles, which provide high-resolution structural characterization of biological complexes(11-13). Such single-particle 3D reconstruction approaches have not yet been successfully applied to the identification of synthetic inorganic nanomaterials with highly symmetrical cage-like shapes. Here, however, using a combination of cryo-electron microscopy and single-particle 3D reconstruction, we suggest the existence of isolated ultrasmall (less than 10 nm) silica cages ('silicages') with dodecahedral structure. We propose that such highly symmetrical, self-assembled cages form through the arrangement of primary silica clusters in aqueous solutions on the surface of oppositely charged surfactant micelles. This discovery paves the way for nanoscale cages made from silica and other inorganic materials to be used as building blocks for a wide range of advanced functional-materials applications

Soluble receptor for advanced glycation end products (sRAGE) as a biomarker of COPD

BACKGROUND: Soluble receptor for advanced glycation end products (sRAGE) is a proposed emphysema and airflow obstruction biomarker; however, previous publications have shown inconsistent associations and only one study has investigate the association between sRAGE and emphysema. No cohorts have examined the association between sRAGE and progressive decline of lung function. There have also been no evaluation of assay compatibility, receiver operating characteristics, and little examination of the effect of genetic variability in non-white population. This manuscript addresses these deficiencies and introduces novel data from Pittsburgh COPD SCCOR and as well as novel work on airflow obstruction. A meta-analysis is used to quantify sRAGE associations with clinical phenotypes. METHODS: sRAGE was measured in four independent longitudinal cohorts on different analytic assays: COPDGene (n = 1443); SPIROMICS (n = 1623); ECLIPSE (n = 2349); Pittsburgh COPD SCCOR (n = 399). We constructed adjusted linear mixed models to determine associations of sRAGE with baseline and follow up forced expiratory volume at one second (FEV1) and emphysema by quantitative high-resolution CT lung density at the 15th percentile (adjusted for total lung capacity). RESULTS: Lower plasma or serum sRAGE values were associated with a COPD diagnosis (P < 0.001), reduced FEV1 (P < 0.001), and emphysema severity (P < 0.001). In an inverse-variance weighted meta-analysis, one SD lower log10-transformed sRAGE was associated with 105 ± 22 mL lower FEV1 and 4.14 ± 0.55 g/L lower adjusted lung density. After adjusting for covariates, lower sRAGE at baseline was associated with greater FEV1 decline and emphysema progression only in the ECLIPSE cohort. Non-Hispanic white subjects carrying the rs2070600 minor allele (A) and non-Hispanic African Americans carrying the rs2071288 minor allele (A) had lower sRAGE measurements compare to those with the major allele, but their emphysema-sRAGE regression slopes were similar. CONCLUSIONS: Lower blood sRAGE is associated with more severe airflow obstruction and emphysema, but associations with progression are inconsistent in the cohorts analyzed. In these cohorts, genotype influenced sRAGE measurements and strengthened variance modelling. Thus, genotype should be included in sRAGE evaluations

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p 10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10−392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group

Cramer-Rao Bounds for Discrete-Time Nonlinear Filtering Problems

A Cramer-Rao bound for the mean squared error that can be achieved with non1inear observations of a nonlinear p-th order autoregressive (AR) process where both the process and observation noise covariances can be state dependent is presented. The major limitation is that the AR process must be driven by an additive white Gaussian noise process that has a full-rank covariance. A numerical example demonstrating the tightness of the bound for a particular problem is included

EXPLICIT ORTHONORMAL BASES FOR FUNCTIONS EXHIBITING THE ROTATIONAL SYMMETRIES OF A PLATONIC SOLID

We compute explicit orthonormal bases for functions invariant under the rotational symmetries of a Platonic solid. Each function in the basis is a linear combination of spherical harmonics. For each symmetry (icosahedral, octahedral, tetrahedral) the calculation has three steps: First derive a bilinear equation for the coefficients by comparing the expansion of a symmetrized delta functioii in both spherical harmonics and the symmetric harmonics. The equation is parameterized by the location (Θ0,Ø0) of the delta function and must be ~at~isfiefodr all locations. Second, express t,he dependence on the delta function location in a Fourier (Ø0) and Taylor (Θ0) series and thereby derive a new system of bilinear equations by comparing selected coefficients. Third, derive a recursive solution of the new system and explicitly solve the recursion with the aid of symbolic computation. The results for the icosahedral case are important for structural studies of small spherical viruses

NONLINEAR MODELING AND PROCESSING OF SPEECH WITH APPLICATIONS TO SPEECH CODING

IN recent years there has been increasing interest in nonlinear speech modeling. In our approach, a speech signal is modeled as a sum of jointly amplitude (AM) and frequency (FM) modulated cosines with slowly-varying center ffrequencies. The key problem is to extract the center frequency and the amplitude and frequency modulations for each formant in the model from the measured speech signals. In this study, we describe the speech signal in terms of statistical models and apply statistical nonlinear filtering techniclues (Extended Kalman Filter) to estimate the amplitude and frequency. The Ahl and Fbl signals are estimated for all the formants simultaneously in an efficient and computationally tractable manner. Using Cra,mer-R.ao 11ound techniques, we can compare the performance of our computationally feasible estimators relative to the performance of the computationally intractable optimal estimator. Recombination of the amplitude aad frequency signals generated by our approach results in faithful reconstruction of speech in both the time and frequency domains. We consider two applications. The first application, which is formant tracking, is a direct application of our non1inear filters since the formant frequencies are a part of our nonlinear model. The application of our entire framework to speech coding is also discussed