Machine learning of hierarchical clustering to segment 2D and 3D images

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.Comment: 15 pages, 8 figure

Modeling and experimental investigations of the stress-softening behavior of soft collagenous tissues

This paper deals with the formulation of a micro-mechanically based dam-age model for soft collagenous tissues. The model is motivated by (i) a sliding filament model proposed in the literature [1] and (ii) by experimental observations from electron microscopy (EM) images of human abdominal aorta specimens, see [2]. Specifically, we derive a continuum damage model that takes into account statistically distributed pro- teoglycan (PG) bridges. The damage model is embedded into the constitutive framework proposed by Balzani et al. [3] and adjusted to cyclic uniaxial tension tests of a hu- man carotid artery. Furthermore, the resulting damage distribution of the model after a circumferential overstretch of a simplified arterial section is analyzed in a finite element calculation

Three-dimensional structure of I(to); Kv4.2-KChIP2 ion channels by electron microscopy at 21 Angstrom resolution.

Regulatory KChIP2 subunits assemble with pore-forming Kv4.2 subunits in 4:4 complexes to produce native voltage-gated potassium (Kv) channels like cardiac I(to) and neuronal I(A) subtypes. Here, negative stain electron microscopy (EM) and single particle averaging reveal KChIP2 to create a novel approximately 35 x 115 x 115 Angstrom, intracellular fenestrated rotunda: four peripheral columns that extend down from the membrane-embedded portion of the channel to enclose the Kv4.2 "hanging gondola" (a platform held beneath the transmembrane conduction pore by four internal columns). To reach the pore from the cytosol, ions traverse one of four external fenestrae to enter the rotundal vestibule and then cross one of four internal windows in the gondola

A local Gaussian filter and adaptive morphology as tools for completing partially discontinuous curves

This paper presents a method for extraction and analysis of curve--type structures which consist of disconnected components. Such structures are found in electron--microscopy (EM) images of metal nanograins, which are widely used in the field of nanosensor technology. The topography of metal nanograins in compound nanomaterials is crucial to nanosensor characteristics. The method of completing such templates consists of three steps. In the first step, a local Gaussian filter is used with different weights for each neighborhood. In the second step, an adaptive morphology operation is applied to detect the endpoints of curve segments and connect them. In the last step, pruning is employed to extract a curve which optimally fits the template

Supplanting crystallography or supplementing microscopy? A combined approach to the study of an enveloped virus

The recent advances in the resolution obtained by single-particle reconstructions from cryo-electron microscopy (cryo-EM) have led to an increase in studies that combine X-ray crystallographic results with those of electron microscopy (EM). Here, such a combination is described in the determination of the structure of an enveloped animal virus, Semliki Forest virus, at 9 Å resolution. The issues of model bias in determination of the structure, the definition of resolution in a single-particle reconstruction, the effect of the correction of the contrast-transfer function on the structure determined and the use of a high-resolution structure of a subunit in the interpretation of the structure of the complex are addressed

Annotating Synapses in Large EM Datasets

Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience and becoming a focus of the emerging field of connectomics. To date, electron microscopy (EM) is the most proven technique for identifying and quantifying synaptic connections. As advances in EM make acquiring larger datasets possible, subsequent manual synapse identification ({\em i.e.}, proofreading) for deciphering a connectome becomes a major time bottleneck. Here we introduce a large-scale, high-throughput, and semi-automated methodology to efficiently identify synapses. We successfully applied our methodology to the Drosophila medulla optic lobe, annotating many more synapses than previous connectome efforts. Our approaches are extensible and will make the often complicated process of synapse identification accessible to a wider-community of potential proofreaders

Postsynaptic protein organization revealed by electron microscopy.

Neuronal synapses are key devices for transmitting and processing information in the nervous system. Synaptic plasticity, generally regarded as the cellular basis of learning and memory, involves changes of subcellular structures that take place at the nanoscale. High-resolution imaging methods, especially electron microscopy (EM), have allowed for quantitative analysis of such nanoscale structures in different types of synapses. In particular, the semi-ordered organization of neurotransmitter receptors and their interacting scaffolds in the postsynaptic density have been characterized for both excitatory and inhibitory synapses by studies using various EM techniques such as immuno-EM, electron tomography of high-pressure freezing and freeze-substituted samples, and cryo electron tomography. These techniques, in combination with new correlative approaches, will further facilitate our understanding of the molecular organization underlying diverse functions of neuronal synapses

United we stand: combining structural methods

Structural biologists benefit enormously by combining structural approaches to tackle biological systems. This is evident in the increasing use of complementary methods combined with the traditional structural biology techniques of macromolecular X-ray crystallography (MX), nuclear magnetic resonance (NMR) and electron microscopy (EM) to generate structural information

Interactions among mitochondrial proteins altered in glioblastoma

Mitochondrial dysfunction is putatively central to glioblastoma (GBM) pathophysiology but there has been no systematic analysis in GBM of the proteins which are integral to mitochondrial function. Alterations in proteins in mitochondrial enriched fractions from patients with GBM were defined with label-free liquid chromatography mass spectrometry. 256 mitochondrially-associated proteins were identified in mitochondrial enriched fractions and 117 of these mitochondrial proteins were markedly (fold-change ≥2) and significantly altered in GBM (p ≤ 0.05). Proteins associated with oxidative damage (including catalase, superoxide dismutase 2, peroxiredoxin 1 and peroxiredoxin 4) were increased in GBM. Protein–protein interaction analysis highlighted a reduction in multiple proteins coupled to energy metabolism (in particular respiratory chain proteins, including 23 complex-I proteins). Qualitative ultrastructural analysis in GBM with electron microscopy showed a notably higher prevalence of mitochondria with cristolysis in GBM. This study highlights the complex mitochondrial proteomic adjustments which occur in GBM pathophysiology