Distributional Robustness of K-class Estimators and the PULSE

Recently, in causal discovery, invariance properties such as the moment criterion which two-stage least square estimator leverage have been exploited for causal structure learning: e.g., in cases, where the causal parameter is not identifiable, some structure of the non-zero components may be identified, and coverage guarantees are available. Subsequently, anchor regression has been proposed to trade-off invariance and predictability. The resulting estimator is shown to have optimal predictive performance under bounded shift interventions. In this paper, we show that the concepts of anchor regression and K-class estimators are closely related. Establishing this connection comes with two benefits: (1) It enables us to prove robustness properties for existing K-class estimators when considering distributional shifts. And, (2), we propose a novel estimator in instrumental variable settings by minimizing the mean squared prediction error subject to the constraint that the estimator lies in an asymptotically valid confidence region of the causal parameter. We call this estimator PULSE (p-uncorrelated least squares estimator) and show that it can be computed efficiently, even though the underlying optimization problem is non-convex. We further prove that it is consistent. We perform simulation experiments illustrating that there are several settings including weak instrument settings, where PULSE outperforms other estimators and suffers from less variability.Comment: 85 pages, 15 figure

A Convex Reconstruction Model for X-ray Tomographic Imaging with Uncertain Flat-fields

Classical methods for X-ray computed tomography are based on the assumption that the X-ray source intensity is known, but in practice, the intensity is measured and hence uncertain. Under normal operating conditions, when the exposure time is sufficiently high, this kind of uncertainty typically has a negligible effect on the reconstruction quality. However, in time- or dose-limited applications such as dynamic CT, this uncertainty may cause severe and systematic artifacts known as ring artifacts. By carefully modeling the measurement process and by taking uncertainties into account, we derive a new convex model that leads to improved reconstructions despite poor quality measurements. We demonstrate the effectiveness of the methodology based on simulated and real data sets.Comment: Accepted at IEEE Transactions on Computational Imagin

Cellular Factors That Shape the Activity or Function of Nitric Oxide-Stimulated Soluble Guanylyl Cyclase

NO-stimulated guanylyl cyclase (SGC) is a hemoprotein that plays key roles in various physiological functions. SGC is a typical enzyme-linked receptor that combines the functions of a sensor for NO gas and cGMP generator. SGC possesses exclusive selectivity for NO and exhibits a very fast binding of NO, which allows it to function as a sensitive NO receptor. This review describes the effect of various cellular factors, such as additional NO, cell thiols, cell-derived small molecules and proteins on the function of SGC as cellular NO receptor. Due to its vital physiological function SGC is an important drug target. An increasing number of synthetic compounds that affect SGC activity via different mechanisms are discovered and brought to clinical trials and clinics. Cellular factors modifying the activity of SGC constitute an opportunity for improving the effectiveness of existing SGC-directed drugs and/or the creation of new therapeutic strategies

Soluble Guanylyl Cyclase: Molecular Basis for Ligand Selectivity and Action In Vitro and In Vivo

Nitric oxide (NO), carbon monoxide (CO), oxygen (O2), hydrogen sulfide (H2S) are gaseous molecules that play important roles in the physiology and pathophysiology of eukaryotes. Tissue concentrations of these physiologically relevant gases vary remarkable from nM range for NO to high μM range of O2. Various hemoproteins play a significant role in sensing and transducing cellular signals encoded by gaseous molecules or in transporting them. Soluble guanylyl cyclase (sGC) is a hemoprotein that plays vital roles in a wide range of physiological functions and combines the functions of gaseous sensor and signal transducer. sGC uniquely evolved to sense low non-toxic levels of NO and respond to elevated NO levels by increasing its catalytic ability to generate the secondary signaling messenger cyclic guanosine monophosphate (cGMP). This review discusses sGC\u27s gaseous ligand selectivity and the molecular basis for sGC function as high-affinity and selectivity NO receptor. The effects of other gaseous molecules and small molecules of cellular origin on sGC\u27s function are also discussed