Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

Twenty questions with noise: Bayes optimal policies for entropy loss

We consider the problem of twenty questions with noisy answers, in which we seek to find a target by repeatedly choosing a set, asking an oracle whether the target lies in this set, and obtaining an answer corrupted by noise. Starting with a prior distribution on the target's location, we seek to minimize the expected entropy of the posterior distribution. We formulate this problem as a dynamic program and show that any policy optimizing the one-step expected reduction in entropy is also optimal over the full horizon. Two such Bayes optimal policies are presented: one generalizes the probabilistic bisection policy due to Horstein and the other asks a deterministic set of questions. We study the structural properties of the latter, and illustrate its use in a computer vision application.

Consensus on the Need for a Hierarchical List of Patient-reported Pain Outcomes for Metaanalyses of Knee Osteoarthritis Trials: An OMERACT Objective.

OBJECTIVE Although protocol registration for systematic reviews is still not mandatory, reviewers should be strongly encouraged to register the protocol to identify the methodological approach, including all outcomes of interest. This will minimize the likelihood of biased decisions in reviews, such as selective outcome reporting. A group of international experts convened to address issues regarding the need to develop hierarchical lists of outcome measurement instruments for a particular outcome for metaanalyses. METHODS Multiple outcome measurement instruments exist to measure the same outcome. Metaanalysis of knee osteoarthritis (OA) trials, and the assessment of pain as an outcome, was used as an exemplar to assess how Outcome Measures in Rheumatology (OMERACT), the Cochrane Collaboration, and other international initiatives might contribute in this area. The meeting began with formal presentations of background topics, empirical evidence from the literature, and a brief introduction to 2 existing hierarchical lists of pain outcome measurement instruments recommended for metaanalyses of knee OA trials. RESULTS After discussions, most participants agreed that there is a need to develop a methodology for generation of hierarchical lists of outcome measurement instruments to guide metaanalyses. Tools that could be used to steer development of such a prioritized list are the COSMIN checklist (Consensus-based Standards for the selection of health status Measurement Instruments) and the OMERACT Filter 2.0. CONCLUSION We list meta-epidemiological research agenda items that address the frequency of reported outcomes in trials, as well as methodologies to assess the best measurement properties (i.e., truth, discrimination, and feasibility).

Hierarchical self-assembly of the DNA-grafted supramolecular polymers

Conjugation of functional entities with a specific set of optical, mechanical or biological properties to DNA strands allows engineering of sophisticated DNA-containing architectures. Among various hybrid systems, DNA-grafted polymers occupy an important place in modern materials science. In this contribution we present the non-covalent synthesis and properties of DNA-grafted linear supramolecular polymers (SPs), which are assembled in a controllable manner from short chimeric DNA-pyrene oligomers. The synthetic oligomers consist of two parts: a 10 nucleotides long DNA chain and a covalently attached segment of variable number of phosphodiester-linked pyrenes. The temperature-dependent formation of DNA-grafted SPs is described by a nucleation-elongation mechanism. The high tendency of pyrenes to aggregate in water, leads to the rapid formation of SPs. The core of the assemblies consists of stacked pyrenes. They form a 1D platform, to which the DNA chains are attached. Combined spectroscopic and microscopic studies reveal that the major driving forces of the polymerization are π-stacking of pyrenes and hydrophobic interactions, and DNA pairing contributes to a lesser extent. AFM and TEM experiments demonstrate that the 1D SPs appear as elongated ribbons with a length of several hundred nanometers. They exhibit an apparent helical structure with a pitch-to-pitch distance of 50±15 nm. Since DNA pairing is a highly selective process, the ongoing studies are aimed to utilize DNA-grafted SPs for the programmable arrangement of functional entities. For example, the addition of non-modified complementary DNA strands to the DNA-grafted SPs leads to the cooperative formation of higher-order assemblies. Also, our experiments suggest that the fluorescent pyrene core of 1D ribbons serves as an efficient donor platform for energy transfer applications.

Hierarchical self-assembly of the DNA-grafted supramolecular polymers

Conjugation of functional entities with a specific set of optical, mechanical or biological properties to DNA strands allows engineering of sophisticated DNA-containing architectures. Among various hybrid systems, DNA-grafted polymers occupy an important place in modern materials science. In this contribution we present the non-covalent synthesis and properties of DNA-grafted linear supramolecular polymers (SPs), which are assembled in a controllable manner from short chimeric DNA-pyrene oligomers. The synthetic oligomers consist of two parts: a 10 nucleotides long DNA chain and a covalently attached segment of variable number of phosphodiester-linked pyrenes. The temperature-dependent formation of DNA-grafted SPs is described by a nucleation-elongation mechanism. The high tendency of pyrenes to aggregate in water, leads to the rapid formation of SPs. The core of the assemblies consists of stacked pyrenes. They form a 1D platform, to which the DNA chains are attached. Combined spectroscopic and microscopic studies reveal that the major driving forces of the polymerization are π-stacking of pyrenes and hydrophobic interactions, and DNA pairing contributes to a lesser extent. AFM and TEM experiments demonstrate that the 1D SPs appear as elongated ribbons with a length of several hundred nanometers. They exhibit an apparent helical structure with a pitch-to-pitch distance of 50±15 nm. Since DNA pairing is a highly selective process, the ongoing studies are aimed to utilize DNA-grafted SPs for the programmable arrangement of functional entities. For example, the addition of non-modified complementary DNA strands to the DNA-grafted SPs leads to the cooperative formation of higher-order assemblies. Also, our experiments suggest that the fluorescent pyrene core of 1D ribbons serves as an efficient donor platform for energy transfer applications.

From Ribbons to Networks: Hierarchical Organization of DNA-Grafted Supramolecular Polymers

DNA-grafted supramolecular polymers (SPs) allow the programmed organization of DNA in a highly regular, one-dimensional array. Oligonucleotides are arranged along the edges of pyrene-based helical polymers. Addition of complementary oligonucleotides triggers the assembly of individual nanoribbons resulting in the development of extended supramolecular networks. Network formation is enabled by cooperative coaxial stacking interactions of terminal GC base pairs. The process is accompanied by structural changes in the pyrene polymer core that can be followed spectroscopically. Network formation is reversible, and disassembly into individual ribbons is realized either via thermal denaturation or by addition of a DNA separator strand.

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^

Energy-Entropy-Momentum integration of discrete thermo-visco-elastic dynamics.

A novel time integration scheme is presented for the numerical solution of the dynamics of discrete systems consisting of point masses and thermo-visco-elastic springs. Even considering fully coupled constitutive laws for the elements, the obtained solutions strictly preserve the two laws of thermo dynamics and the symmetries of the continuum evolution equations. Moreover, the unconditional control over the energy and the entropy growth have the effect of stabilizing the numerical solution, allowing the use of larger time steps than those suitable for comparable implicit algorithms. Proofs for these claims are provided in the article as well as numerical examples that illustrate the performance of the method.

3D Soil Images Structure Quantification using Relative Entropy

Soil voids manifest the cumulative effect of local pedogenic processes and ultimately influence soil behavior - especially as it pertains to aeration and hydrophysical properties. Because of the relatively weak attenuation of X-rays by air, compared with liquids or solids, non-disruptive CT scanning has become a very attractive tool for generating three-dimensional imagery of soil voids. One of the main steps involved in this analysis is the thresholding required to transform the original (greyscale) images into the type of binary representation (e.g., pores in white, solids in black) needed for fractal analysis or simulation with Lattice?Boltzmann models (Baveye et al., 2010). The objective of the current work is to apply an innovative approach to quantifying soil voids and pore networks in original X-ray CT imagery using Relative Entropy (Bird et al., 2006; Tarquis et al., 2008). These will be illustrated using typical imagery representing contrasting soil structures. Particular attention will be given to the need to consider the full 3D context of the CT imagery, as well as scaling issues, in the application and interpretation of this index.

Approximate entropy of network parameters

We study the notion of approximate entropy within the framework of network theory. Approximate entropy is an uncertainty measure originally proposed in the context of dynamical systems and time series. We first define a purely structural entropy obtained by computing the approximate entropy of the so-called slide sequence. This is a surrogate of the degree sequence and it is suggested by the frequency partition of a graph. We examine this quantity for standard scale-free and Erdös-Rényi networks. By using classical results of Pincus, we show that our entropy measure often converges with network size to a certain binary Shannon entropy. As a second step, with specific attention to networks generated by dynamical processes, we investigate approximate entropy of horizontal visibility graphs. Visibility graphs allow us to naturally associate with a network the notion of temporal correlations, therefore providing the measure a dynamical garment. We show that approximate entropy distinguishes visibility graphs generated by processes with different complexity. The result probes to a greater extent these networks for the study of dynamical systems. Applications to certain biological data arising in cancer genomics are finally considered in the light of both approaches.