308 research outputs found
Parametric oscillator based on non-linear vortex dynamics in low resistance magnetic tunnel junctions
Radiofrequency vortex spin-transfer oscillators based on magnetic tunnel
junctions with very low resistance area product were investigated. A high power
of excitations has been obtained characterized by a power spectral density
containing a very sharp peak at the fundamental frequency and a series of
harmonics. The observed behaviour is ascribed to the combined effect of spin
transfer torque and Oersted-Amp\`ere field generated by the large applied
dc-current. We furthermore show that the synchronization of a vortex
oscillation by applying a ac bias current is mostly efficient when the external
frequency is twice the oscillator fundamental frequency. This result is
interpreted in terms of a parametric oscillator.Comment: 4 pages, 4 figure
DRAMMS: deformable registration via attribute matching and mutual-saliency weighting
A general-purpose deformable registration algorithm referred to as ”DRAMMS” is presented in this paper. DRAMMS adds to the literature of registration methods that bridge between the traditional voxel-wise methods and landmark/feature-based methods. In particular, DRAMMS extracts Gabor attributes at each voxel and selects the optimal components, so that they form a highly distinctive morphological signature reflecting the anatomical context around each voxel in a multi-scale and multi-resolution fashion. Compared with intensity or mutual-information based methods, the high-dimensional optimal Gabor attributes render different anatomical regions relatively distinctively identifiable and therefore help establish more accurate and reliable correspondence. Moreover, the optimal Gabor attribute vector is constructed in a way that generalizes well, i.e., it can be applied to different registration tasks, regardless of the image contents under registration. A second characteristic of DRAMMS is that it is based on a cost function that weights different voxel pairs according to a metric referred to as ”mutual-saliency”, which reflects the uniqueness (reliability) of anatomical correspondences implied by the tentative transformation. As a result, image voxels do not contribute equally to the optimization process, as in most voxel-wise methods, or in a binary selection fashion, as in most landmark/feature-based methods. Instead, they contribute according to a continuously-valued mutual-saliency map, which is dynamically updated during the algorithm’s evolution. The general applicability and accuracy of DRAMMS are demonstrated by experiments in simulated images, inter-subject images, single-/multi-modality images, and longitudinal images, from human and mouse brains, breast, heart, and prostate
Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks
Both image registration and label fusion in the multi-atlas segmentation
(MAS) rely on the intensity similarity between target and atlas images.
However, such similarity can be problematic when target and atlas images are
acquired using different imaging protocols. High-level structure information
can provide reliable similarity measurement for cross-modality images when
cooperating with deep neural networks (DNNs). This work presents a new MAS
framework for cross-modality images, where both image registration and label
fusion are achieved by DNNs. For image registration, we propose a consistent
registration network, which can jointly estimate forward and backward dense
displacement fields (DDFs). Additionally, an invertible constraint is employed
in the network to reduce the correspondence ambiguity of the estimated DDFs.
For label fusion, we adapt a few-shot learning network to measure the
similarity of atlas and target patches. Moreover, the network can be seamlessly
integrated into the patch-based label fusion. The proposed framework is
evaluated on the MM-WHS dataset of MICCAI 2017. Results show that the framework
is effective in both cross-modality registration and segmentation
Profound Effect of Profiling Platform and Normalization Strategy on Detection of Differentially Expressed MicroRNAs – A Comparative Study
Adequate normalization minimizes the effects of systematic technical variations and is a prerequisite for getting meaningful biological changes. However, there is inconsistency about miRNA normalization performances and recommendations. Thus, we investigated the impact of seven different normalization methods (reference gene index, global geometric mean, quantile, invariant selection, loess, loessM, and generalized procrustes analysis) on intra- and inter-platform performance of two distinct and commonly used miRNA profiling platforms.We included data from miRNA profiling analyses derived from a hybridization-based platform (Agilent Technologies) and an RT-qPCR platform (Applied Biosystems). Furthermore, we validated a subset of miRNAs by individual RT-qPCR assays. Our analyses incorporated data from the effect of differentiation and tumor necrosis factor alpha treatment on primary human skeletal muscle cells and a murine skeletal muscle cell line. Distinct normalization methods differed in their impact on (i) standard deviations, (ii) the area under the receiver operating characteristic (ROC) curve, (iii) the similarity of differential expression. Loess, loessM, and quantile analysis were most effective in minimizing standard deviations on the Agilent and TLDA platform. Moreover, loess, loessM, invariant selection and generalized procrustes analysis increased the area under the ROC curve, a measure for the statistical performance of a test. The Jaccard index revealed that inter-platform concordance of differential expression tended to be increased by loess, loessM, quantile, and GPA normalization of AGL and TLDA data as well as RGI normalization of TLDA data.We recommend the application of loess, or loessM, and GPA normalization for miRNA Agilent arrays and qPCR cards as these normalization approaches showed to (i) effectively reduce standard deviations, (ii) increase sensitivity and accuracy of differential miRNA expression detection as well as (iii) increase inter-platform concordance. Results showed the successful adoption of loessM and generalized procrustes analysis to one-color miRNA profiling experiments
Separate cortical stages in amodal completion revealed by functional magnetic resonance adaptation
<p>Abstract</p> <p>Background</p> <p>Objects in our environment are often partly occluded, yet we effortlessly perceive them as whole and complete. This phenomenon is called visual amodal completion. Psychophysical investigations suggest that the process of completion starts from a representation of the (visible) physical features of the stimulus and ends with a completed representation of the stimulus. The goal of our study was to investigate both stages of the completion process by localizing both brain regions involved in processing the physical features of the stimulus as well as brain regions representing the completed stimulus.</p> <p>Results</p> <p>Using fMRI adaptation we reveal clearly distinct regions in the visual cortex of humans involved in processing of amodal completion: early visual cortex – presumably V1 -processes the local contour information of the stimulus whereas regions in the inferior temporal cortex represent the completed shape. Furthermore, our data suggest that at the level of inferior temporal cortex information regarding the original local contour information is not preserved but replaced by the representation of the amodally completed percept.</p> <p>Conclusion</p> <p>These findings provide neuroimaging evidence for a multiple step theory of amodal completion and further insights into the neuronal correlates of visual perception.</p
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