The Iteratively Regularized Gau{\ss}-Newton Method with Convex Constraints and Applications in 4Pi-Microscopy

This paper is concerned with the numerical solution of nonlinear ill-posed operator equations involving convex constraints. We study a Newton-type method which consists in applying linear Tikhonov regularization with convex constraints to the Newton equations in each iteration step. Convergence of this iterative regularization method is analyzed if both the operator and the right hand side are given with errors and all error levels tend to zero. Our study has been motivated by the joint estimation of object and phase in 4Pi microscopy, which leads to a semi-blind deconvolution problem with nonnegativity constraints. The performance of the proposed algorithm is illustrated both for simulated and for three-dimensional experimental data

Convergence rates in expectation for Tikhonov-type regularization of Inverse Problems with Poisson data

In this paper we study a Tikhonov-type method for ill-posed nonlinear operator equations \gdag = F( ag) where \gdag is an integrable, non-negative function. We assume that data are drawn from a Poisson process with density t\gdag where $t>0$ may be interpreted as an exposure time. Such problems occur in many photonic imaging applications including positron emission tomography, confocal fluorescence microscopy, astronomic observations, and phase retrieval problems in optics. Our approach uses a Kullback-Leibler-type data fidelity functional and allows for general convex penalty terms. We prove convergence rates of the expectation of the reconstruction error under a variational source condition as $t\to\infty$ both for an a priori and for a Lepski{\u\i}-type parameter choice rule

Tumor Necrosis Factor Alpha Mediates GABAA Receptor Trafficking to the Plasma Membrane of Spinal Cord Neurons In Vivo

The proinflammatory cytokine TNFα contributes to cell death in central nervous system (CNS) disorders by altering synaptic neurotransmission. TNFα contributes to excitotoxicity by increasing GluA2-lacking AMPA receptor (AMPAR) trafficking to the neuronal plasma membrane. In vitro, increased AMPAR on the neuronal surface after TNFα exposure is associated with a rapid internalization of GABAA receptors (GABAARs), suggesting complex timing and dose dependency of the CNS's response to TNFα. However, the effect of TNFα on GABAAR trafficking in vivo remains unclear. We assessed the effect of TNFα nanoinjection on rapid GABAAR changes in rats (N = 30) using subcellular fractionation, quantitative western blotting, and confocal microscopy. GABAAR protein levels in membrane fractions of TNFα and vehicle-treated subjects were not significantly different by Western Blot, yet high-resolution quantitative confocal imaging revealed that TNFα induces GABAAR trafficking to synapses in a dose-dependent manner by 60 min. TNFα-mediated GABAAR trafficking represents a novel target for CNS excitotoxicity

Correct quantum chemistry in a minimal basis from effective Hamiltonians

We describe how to create ab-initio effective Hamiltonians that qualitatively describe correct chemistry even when used with a minimal basis. The Hamiltonians are obtained by folding correlation down from a large parent basis into a small, or minimal, target basis, using the machinery of canonical transformations. We demonstrate the quality of these effective Hamiltonians to correctly capture a wide range of excited states in water, nitrogen, and ethylene, and to describe ground and excited state bond-breaking in nitrogen and the chromium dimer, all in small or minimal basis sets

Syndromics: A Bioinformatics Approach for Neurotrauma Research

Substantial scientific progress has been made in the past 50 years in delineating many of the biological mechanisms involved in the primary and secondary injuries following trauma to the spinal cord and brain. These advances have highlighted numerous potential therapeutic approaches that may help restore function after injury. Despite these advances, bench-to-bedside translation has remained elusive. Translational testing of novel therapies requires standardized measures of function for comparison across different laboratories, paradigms, and species. Although numerous functional assessments have been developed in animal models, it remains unclear how to best integrate this information to describe the complete translational “syndrome” produced by neurotrauma. The present paper describes a multivariate statistical framework for integrating diverse neurotrauma data and reviews the few papers to date that have taken an information-intensive approach for basic neurotrauma research. We argue that these papers can be described as the seminal works of a new field that we call “syndromics”, which aim to apply informatics tools to disease models to characterize the full set of mechanistic inter-relationships from multi-scale data. In the future, centralized databases of raw neurotrauma data will enable better syndromic approaches and aid future translational research, leading to more efficient testing regimens and more clinically relevant findings

Petrographical and petrophysical properties of sandstones: statistical analysis as an approach to predict material behaviour and construction suitability

Most studies dealing with material properties of sandstones are based on a small data set. The present study utilizes petrographical and petrophysical data from 22 selected sandstones and ~300 sandstones from the literature to estimate/predict the material and weathering behaviour of characteristic sandstones. Composition and fabric properties were determined from detailed thin section analyses. Statistical methods applied consist of data distributions with whisker plots and linear regression with confidence regions for the petrophysical and weathering properties. To identify similarities between individual sandstones and to define groups of specific sandstone types, principal component and cluster analyses were applied. The results confirm an interaction between the composition, depositional environment, stratigraphic association and diagenesis, which leads to a particular material behaviour of sandstones. Three different types of pore radii distributions are observed, whereby each is derived from different pore space modifications during diagenesis and is associated with specific sandstone types: (1) bimodal with a maximum in capillary and micropores, (2) unimodal unequal with a maximum in smaller capillary pores and (3) unimodal equable with a maximum in larger capillary pores. Each distribution shows specific dependencies to water absorption, salt loading and hygric dilatation. The strength–porosity relationship shows dependence on the content of unstable lithic fragments, grain contact and type of pore radii distribution, cementation and degree of alteration. Sandstones showing a maximum of capillary pores and micropores (bimodal) exhibit a distinct hygric dilatation and low salt resistance. These sandstones are highly immature sublitharenites–litharenites, characterized by altered unstable rock fragments, which show pointed-elongated grain contacts, and some pseudomatrix. Quartz arenites and sublitharenites–litharenites which are strongly compacted and cemented, show unimodal unequal pore radii distributions, low porosity, high strength and a high salt resistance. The presence of swellable clay minerals in sublitharenites–litharenites leads to a medium to high hygric dilatation, whereas quartz arenites show little hygric dilatation. Sandstones with unimodal equal pore radii distribution mostly belong to weakly compacted and cemented mature quartz arenites. These are characterized by high water absorption and high porosity, low to medium strength and a low salt resistance. The data compiled in this study are used to create a sandstone quality catalogue. Since material properties are dependent on many different parameters of influence, the transition between different lithotypes is fluent

Syndromics: a bioinformatics approach for neurotrauma research.

Substantial scientific progress has been made in the past 50 years in delineating many of the biological mechanisms involved in the primary and secondary injuries following trauma to the spinal cord and brain. These advances have highlighted numerous potential therapeutic approaches that may help restore function after injury. Despite these advances, bench-to-bedside translation has remained elusive. Translational testing of novel therapies requires standardized measures of function for comparison across different laboratories, paradigms, and species. Although numerous functional assessments have been developed in animal models, it remains unclear how to best integrate this information to describe the complete translational "syndrome" produced by neurotrauma. The present paper describes a multivariate statistical framework for integrating diverse neurotrauma data and reviews the few papers to date that have taken an information-intensive approach for basic neurotrauma research. We argue that these papers can be described as the seminal works of a new field that we call "syndromics", which aim to apply informatics tools to disease models to characterize the full set of mechanistic inter-relationships from multi-scale data. In the future, centralized databases of raw neurotrauma data will enable better syndromic approaches and aid future translational research, leading to more efficient testing regimens and more clinically relevant findings

LES-AIDED SHAPE OPTIMISATION OF U-BEND CHANNEL

Reynolds-Averaged Navier-Stokes (RANS) simulations are inaccurate in predicting complex flow features (ex: Separation regions), and therefore deriving an optimised shape using the RANS-adjoint framework does not yield a truly optimal geometry. With the purpose of obtaining accurate sensitivity to objective function of interest, we improve the RANS flowfield using the strategy of Singh et al. [1]. This involves multiplying a corrective factor β to the production term in the Spalart-Allmaras (SA) turbulence model equation and solving the inverse problem to determine the appropriate β field, which enables the RANS solution to match the high-fidelity data. The geometry of our interest is the U-Bend which is widely studied in literature in the context of gas turbine cooling, and which is known to be a challenging case for RANS simulations to reproduce. We use the mean flowfield from a large-eddy simulation of the U-Bend geometry as the high-fidelity data to which the RANS flowfield is fit using the β strategy outlined above. We observe a clear improvement in the RANS flowfield by optimising for the β field, the objective function to be minimized being L2-norm of the mean velocity difference between RANS and LES. We further show that adding an additional corrective factor (γ) to the destruction term in the SA turbulence equation and simultaneously optimising for the γ field alongside the β field results in a better match of the RANS flowfield with the corresponding LES flowfield. We also show that surface sensitivity map for the improved LES-aided flowfield varies significantly in comparison to the baseline SA-based flowfield for an objective function of interest, the total pressure loss in the U-Bend