Invariant higher-order variational problems II

Motivated by applications in computational anatomy, we consider a second-order problem in the calculus of variations on object manifolds that are acted upon by Lie groups of smooth invertible transformations. This problem leads to solution curves known as Riemannian cubics on object manifolds that are endowed with normal metrics. The prime examples of such object manifolds are the symmetric spaces. We characterize the class of cubics on object manifolds that can be lifted horizontally to cubics on the group of transformations. Conversely, we show that certain types of non-horizontal geodesics on the group of transformations project to cubics. Finally, we apply second-order Lagrange--Poincar\'e reduction to the problem of Riemannian cubics on the group of transformations. This leads to a reduced form of the equations that reveals the obstruction for the projection of a cubic on a transformation group to again be a cubic on its object manifold.Comment: 40 pages, 1 figure. First version -- comments welcome

Characterization of a c-Rel inhibitor that mediates anticancer properties in hematologic malignancies by blocking NF-κB-controlled oxidative stress responses

NF-\u3baB plays a variety of roles in oncogenesis and immunity that may be beneficial for therapeutic targeting, but strategies to selectively inhibit NF-\u3baB to exert antitumor activity have been elusive. Here, we describe IT-901, a bioactive naphthalenethiobarbiturate derivative that potently inhibits the NF-\u3baB subunit c-Rel. IT-901 suppressed graft-versus-host disease while preserving graft-versus-lymphoma activity during allogeneic transplantation. Further preclinical assessment of IT-901 for the treatment of human B-cell lymphoma revealed antitumor properties in vitro and in vivo without restriction to NF-\u3baB-dependent lymphoma. This nondiscriminatory, antilymphoma effect was attributed to modulation of the redox homeostasis in lymphoma cells resulting in oxidative stress. Moreover, NF-\u3baB inhibition by IT-901 resulted in reduced stimulation of the oxidative stress response gene heme oxygenase-1, and we demonstrated that NF-\u3baB inhibition exacerbated oxidative stress induction to inhibit growth of lymphoma cells. Notably, IT-901 did not elicit increased levels of reactive oxygen species in normal leukocytes, illustrating its cancer selective properties. Taken together, our results provide mechanistic insight and preclinical proof of concept for IT-901 as a novel therapeutic agent to treat human lymphoid tumors and ameliorate graft-versus-host disease

Some Aspects of Latent Structure Analysis

Latent structure models involve real, potentially observable variables and latent, unobservable variables. The framework includes various particular types of model, such as factor analysis, latent class analysis, latent trait analysis, latent profile models, mixtures of factor analysers, state-space models and others. The simplest scenario, of a single discrete latent variable, includes finite mixture models, hidden Markov chain models and hidden Markov random field models. The paper gives a brief tutorial of the application of maximum likelihood and Bayesian approaches to the estimation of parameters within these models, emphasising especially the fact that computational complexity varies greatly among the different scenarios. In the case of a single discrete latent variable, the issue of assessing its cardinality is discussed. Techniques such as the EM algorithm, Markov chain Monte Carlo methods and variational approximations are mentioned

EPIdemiology of Surgery-Associated Acute Kidney Injury (EPIS-AKI) : Study protocol for a multicentre, observational trial

More than 300 million surgical procedures are performed each year. Acute kidney injury (AKI) is a common complication after major surgery and is associated with adverse short-term and long-term outcomes. However, there is a large variation in the incidence of reported AKI rates. The establishment of an accurate epidemiology of surgery-associated AKI is important for healthcare policy, quality initiatives, clinical trials, as well as for improving guidelines. The objective of the Epidemiology of Surgery-associated Acute Kidney Injury (EPIS-AKI) trial is to prospectively evaluate the epidemiology of AKI after major surgery using the latest Kidney Disease: Improving Global Outcomes (KDIGO) consensus definition of AKI. EPIS-AKI is an international prospective, observational, multicentre cohort study including 10 000 patients undergoing major surgery who are subsequently admitted to the ICU or a similar high dependency unit. The primary endpoint is the incidence of AKI within 72 hours after surgery according to the KDIGO criteria. Secondary endpoints include use of renal replacement therapy (RRT), mortality during ICU and hospital stay, length of ICU and hospital stay and major adverse kidney events (combined endpoint consisting of persistent renal dysfunction, RRT and mortality) at day 90. Further, we will evaluate preoperative and intraoperative risk factors affecting the incidence of postoperative AKI. In an add-on analysis, we will assess urinary biomarkers for early detection of AKI. EPIS-AKI has been approved by the leading Ethics Committee of the Medical Council North Rhine-Westphalia, of the Westphalian Wilhelms-University Münster and the corresponding Ethics Committee at each participating site. Results will be disseminated widely and published in peer-reviewed journals, presented at conferences and used to design further AKI-related trials. Trial registration number NCT04165369

Principal component based diffeomorphic surface mapping

10.1109/TMI.2011.2168567IEEE Transactions on Medical Imaging312302-311ITMI

Parallel transport in diffeomorphisms distinguishes the time-dependent pattern of hippocampal surface deformation due to healthy aging and the dementia of the Alzheimer's type

10.1016/j.neuroimage.2007.11.041NeuroImage40168-76NEIM

A Hierarchical Geodesic Model for Diffeomorphic Longitudinal Shape Analysis

Abstract. Hierarchical linear models (HLMs) are a standard approach for analyzing data where individuals are measured repeatedly over time. However, such models are only applicable to longitudinal studies of Eu-clidean data. In this paper, we propose a novel hierarchical geodesic model (HGM), which generalizes HLMs to the manifold setting. Our proposed model explains the longitudinal trends in shapes represented as elements of the group of diffeomorphisms. The individual level geodesics represent the trajectory of shape changes within individuals. The group level geodesic represents the average trajectory of shape changes for the population. We derive the solution of HGMs on diffeomorphisms to es-timate individual level geodesics, the group geodesic, and the residual geodesics. We demonstrate the effectiveness of HGMs for longitudinal analysis of synthetically generated shapes and 3D MRI brain scans