Clustering Assessment: Enhancing Synergies among Multilateral Environmental Agreements

Against the background of a widely fragmented and diluted international environmental governance architecture, different reform options are currently being discussed. This issue brief considers whether streamlining international environmental regimes by grouping or ‘clustering’ international agreements could improve effectiveness and efficiency. It outlines the general idea of the clustering approach, draws lessons from the chemicals and waste cluster and examines the implications and potentials of clustering multilateral environmental agreements.

Clustering subspecies of Aeromonas salmonicida using IS630 typing.

BACKGROUND The insertion element IS630 found in Aeromonas salmonicida belongs to the IS630-Tc1-mariner superfamily of transposons. It is present in multiple copies and represents approximately half of the IS present in the genome of A. salmonicida subsp. salmonicida A449. RESULTS By using High Copy Number IS630 Restriction Fragment Length Polymorphism (HCN-IS630-RFLP), strains of various subspecies of Aeromonas salmonicida showed conserved or clustering patterns, thus allowing their differentiation from each other. Fingerprints of A. salmonicida subsp. salmonicida showed the highest homogeneity while 'atypical' A. salmonicida strains were more heterogeneous. IS630 typing also differentiated A. salmonicida from other Aeromonas species. The copy number of IS630 in Aeromonas salmonicida ranges from 8 to 35 and is much lower in other Aeromonas species. CONCLUSIONS HCN-IS630-RFLP is a powerful tool for subtyping of A. salmonicida. The high stability of IS630 insertions in A. salmonicida subsp. salmonicida indicates that it might have played a role in pathoadaptation of A. salmonicida which has reached an optimal configuration in the highly virulent and specific fish pathogen A. salmonicida subsp. salmonicida.

Whole Proteome Analysis of the Protozoan Parasite Trypanosoma brucei using Stable Isotope Labeling by Amino Acids in Cell Culture and Mass Spectrometry

The single-celled protozoan Trypanosoma brucei spp. is the causative agent of human African trypanosomiasis and nagana in cattle. Quantitative proteomics for the first time allowed for the characterization of the proteome from several different life stages of the parasite (1-3). To achieve this, stable isotope labeling by amino acids in cell culture (SILAC; (4)) was adapted to T. brucei spp. cultures. T. brucei cells grown in standard media with dialyzed fetal calf serum containing heavy isotope-labeled amino acids (arginine and lysine) show efficient incorporation of the labeled amino acids into the whole cell proteome (8-12 divisions) and no detectable amino acid conversions. The method can be applied to both of the major life stages of the parasite and in combination with RNAi or gene knock-out approaches.

Hierarchical accumulation of RyR post-translational modifications drives disease progression in dystrophic cardiomyopathy

AIMS:Duchenne muscular dystrophy (DMD) is a muscle disease with serious cardiac complications. Changes in Ca(2+) homeostasis and oxidative stress were recently associated with cardiac deterioration, but the cellular pathophysiological mechanisms remain elusive. We investigated whether the activity of ryanodine receptor (RyR) Ca(2+) release channels is affected, whether changes in function are cause or consequence and which post-translational modifications drive disease progression. METHODS AND RESULTS:Electrophysiological, imaging, and biochemical techniques were used to study RyRs in cardiomyocytes from mdx mice, an animal model of DMD. Young mdx mice show no changes in cardiac performance, but do so after ∼8 months. Nevertheless, myocytes from mdx pups exhibited exaggerated Ca(2+) responses to mechanical stress and 'hypersensitive' excitation-contraction coupling, hallmarks of increased RyR Ca(2+) sensitivity. Both were normalized by antioxidants, inhibitors of NAD(P)H oxidase and CaMKII, but not by NO synthases and PKA antagonists. Sarcoplasmic reticulum Ca(2+) load and leak were unchanged in young mdx mice. However, by the age of 4-5 months and in senescence, leak was increased and load was reduced, indicating disease progression. By this age, all pharmacological interventions listed above normalized Ca(2+) signals and corrected changes in ECC, Ca(2+) load, and leak. CONCLUSION:Our findings suggest that increased RyR Ca(2+) sensitivity precedes and presumably drives the progression of dystrophic cardiomyopathy, with oxidative stress initiating its development. RyR oxidation followed by phosphorylation, first by CaMKII and later by PKA, synergistically contributes to cardiac deterioration.

Hierarchical segmentation-assisted multimodal registration for MR brain images

Information theory-based metric such as mutual information (MI) is widely used as similarity measurement for multimodal registration. Nevertheless, this metric may lead to matching ambiguity for non-rigid registration. Moreover, maximization of MI alone does not necessarily produce an optimal solution. In this paper, we propose a segmentation-assisted similarity metric based on point-wise mutual information (PMI). This similarity metric, termed SPMI, enhances the registration accuracy by considering tissue classification probabilities as prior information, which is generated from an expectation maximization (EM) algorithm. Diffeomorphic demons is then adopted as the registration model and is optimized in a hierarchical framework (H-SPMI) based on different levels of anatomical structure as prior knowledge. The proposed method is evaluated using Brainweb synthetic data and clinical fMRI images. Both qualitative and quantitative assessment were performed as well as a sensitivity analysis to the segmentation error. Compared to the pure intensity-based approaches which only maximize mutual information, we show that the proposed algorithm provides significantly better accuracy on both synthetic and clinical data.

Fully Automatic Segmentation of AP Pelvis X-rays via Random Forest Regression and Hierarchical Sparse Shape Composition

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 and robust approach for landmarking and segmentation of both pelvis and femur in a conventional AP X-ray. Our approach is based on random forest regression and hierarchical sparse shape composition. Experiments conducted on 436 clinical AP pelvis x-rays show that our approach achieves an average point-to-curve error around 1.3 mm for femur and 2.2 mm for pelvis, both with success rates around 98%. Compared to existing methods, our approach exhibits better performance in both the robustness and the accuracy.

Low rank subspace clustering (LRSC)

We consider the problem of fitting a union of subspaces to a collection of data points drawn from one or more subspaces and corrupted by noise and/or gross errors. We pose this problem as a non-convex optimization problem, where the goal is to decompose the corrupted data matrix as the sum of a clean and self-expressive dictionary plus a matrix of noise and/or gross errors. By self-expressive we mean a dictionary whose atoms can be expressed as linear combinations of themselves with low-rank coefficients. In the case of noisy data, our key contribution is to show that this non-convex matrix decomposition problem can be solved in closed form from the SVD of the noisy data matrix. The solution involves a novel polynomial thresholding operator on the singular values of the data matrix, which requires minimal shrinkage. For one subspace, a particular case of our framework leads to classical PCA, which requires no shrinkage. For multiple subspaces, the low-rank coefficients obtained by our framework can be used to construct a data affinity matrix from which the clustering of the data according to the subspaces can be obtained by spectral clustering. In the case of data corrupted by gross errors, we solve the problem using an alternating minimization approach, which combines our polynomial thresholding operator with the more traditional shrinkage-thresholding operator. Experiments on motion segmentation and face clustering show that our framework performs on par with state-of-the-art techniques at a reduced computational cost.

A Hierarchical Bayesian Model for Measuring Motion Adaptation

Previous research has shown that motion imagery draws on the same neural circuits that are involved in perception of motion, thus leading to a motion aftereffect (Winawer et al., 2010). Imagined stimuli can induce a similar shift in participants’ psychometric functions as neural adaptation due to a perceived stimulus. However, these studies have been criticized on the grounds that they fail to exclude the possibility that the subjects might have guessed the experimental hypothesis, and behaved accordingly (Morgan et al., 2012). In particular, the authors claim that participants can adopt arbitrary response criteria, which results in similar changes of the central tendency μ of psychometric curves as those shown by Winawer et al. (2010).