1,568 research outputs found
Extracting the Structure and Conformations of Biological Entities from Large Datasets
In biology, structure determines function, which often proceeds via changes in conformation. Efficient means for determining structure exist, but mapping conformations continue to present a serious challenge. Single-particles approaches, such as cryogenic electron microscopy (cryo-EM) and emerging diffract & destroy X-ray techniques are, in principle, ideally positioned to overcome these challenges. But the algorithmic ability to extract information from large heterogeneous datasets consisting of unsorted snapshots - each emanating from an unknown orientation of an object in an unknown conformation - remains elusive.
It is the objective of this thesis to describe and validate a powerful suite of manifold-based algorithms able to extract structural and conformational information from large datasets. These computationally efficient algorithms offer a new approach to determining the structure and conformations of viruses and macromolecules.
After an introduction, we demonstrate a distributed, exact k-Nearest Neighbor Graph (k-NNG) construction method, in order to establish a firm algorithmic basis for manifold-based analysis. The proposed algorithm uses Graphics Processing Units (GPUs) and exploits multiple levels of parallelism in distributed computational environment and it is scalable for different cluster sizes, with each compute node in the cluster containing multiple GPUs.
Next, we present applications of manifold-based analysis in determining structure and conformational variability. Using the Diffusion Map algorithm, a new approach is presented, which is capable of determining structure of symmetric objects, such as viruses, to 1/100th of the object diameter, using low-signal diffraction snapshots. This is demonstrated by means of a successful 3D reconstruction of the Satellite Tobacco Necrosis Virus (STNV) to atomic resolution from simulated diffraction snapshots with and without noise.
We next present a new approach for determining discrete conformational changes of the enzyme Adenylate kinase (ADK) from very large datasets of up to 20 million snapshots, each with ~104 pixels. This exceeds by an order of magnitude the largest dataset previously analyzed.
Finally, we present a theoretical framework and an algorithmic pipeline for capturing continuous conformational changes of the ribosome from ultralow-signal (-12dB) experimental cryo-EM. Our analysis shows a smooth, concerted change in molecular structure in two-dimensional projection, which might be indicative of the way the ribosome functions as a molecular machine.
The thesis ends with a summary and future prospects
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Convolutional neural networks have recently demonstrated high-quality
reconstruction for single-image super-resolution. In this paper, we propose the
Laplacian Pyramid Super-Resolution Network (LapSRN) to progressively
reconstruct the sub-band residuals of high-resolution images. At each pyramid
level, our model takes coarse-resolution feature maps as input, predicts the
high-frequency residuals, and uses transposed convolutions for upsampling to
the finer level. Our method does not require the bicubic interpolation as the
pre-processing step and thus dramatically reduces the computational complexity.
We train the proposed LapSRN with deep supervision using a robust Charbonnier
loss function and achieve high-quality reconstruction. Furthermore, our network
generates multi-scale predictions in one feed-forward pass through the
progressive reconstruction, thereby facilitates resource-aware applications.
Extensive quantitative and qualitative evaluations on benchmark datasets show
that the proposed algorithm performs favorably against the state-of-the-art
methods in terms of speed and accuracy.Comment: This work is accepted in CVPR 2017. The code and datasets are
available on http://vllab.ucmerced.edu/wlai24/LapSRN
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework
We propose MeshfreeFlowNet, a novel deep learning-based super-resolution
framework to generate continuous (grid-free) spatio-temporal solutions from the
low-resolution inputs. While being computationally efficient, MeshfreeFlowNet
accurately recovers the fine-scale quantities of interest. MeshfreeFlowNet
allows for: (i) the output to be sampled at all spatio-temporal resolutions,
(ii) a set of Partial Differential Equation (PDE) constraints to be imposed,
and (iii) training on fixed-size inputs on arbitrarily sized spatio-temporal
domains owing to its fully convolutional encoder. We empirically study the
performance of MeshfreeFlowNet on the task of super-resolution of turbulent
flows in the Rayleigh-Benard convection problem. Across a diverse set of
evaluation metrics, we show that MeshfreeFlowNet significantly outperforms
existing baselines. Furthermore, we provide a large scale implementation of
MeshfreeFlowNet and show that it efficiently scales across large clusters,
achieving 96.80% scaling efficiency on up to 128 GPUs and a training time of
less than 4 minutes.Comment: Supplementary Video: https://youtu.be/mjqwPch9gDo. Accepted to SC2
Tenfold your photons -- a physically-sound approach to filtering-based variance reduction of Monte-Carlo-simulated dose distributions
X-ray dose constantly gains interest in the interventional suite. With dose
being generally difficult to monitor reliably, fast computational methods are
desirable. A major drawback of the gold standard based on Monte Carlo (MC)
methods is its computational complexity. Besides common variance reduction
techniques, filter approaches are often applied to achieve conclusive results
within a fraction of time. Inspired by these methods, we propose a novel
approach. We down-sample the target volume based on the fraction of mass,
simulate the imaging situation, and then revert the down-sampling. To this end,
the dose is weighted by the mass energy absorption, up-sampled, and distributed
using a guided filter. Eventually, the weighting is inverted resulting in
accurate high resolution dose distributions. The approach has the potential to
considerably speed-up MC simulations since less photons and boundary checks are
necessary. First experiments substantiate these assumptions. We achieve a
median accuracy of 96.7 % to 97.4 % of the dose estimation with the proposed
method and a down-sampling factor of 8 and 4, respectively. While maintaining a
high accuracy, the proposed method provides for a tenfold speed-up. The overall
findings suggest the conclusion that the proposed method has the potential to
allow for further efficiency.Comment: 6 pages, 3 figures, Bildverarbeitung f\"ur die Medizin 202
Antipodally invariant metrics for fast regression-based super-resolution
Dictionary-based super-resolution (SR) algorithms usually select dictionary atoms based on the distance or similarity metrics. Although the optimal selection of the nearest neighbors is of central importance for such methods, the impact of using proper metrics for SR has been overlooked in literature, mainly due to the vast usage of Euclidean distance. In this paper, we present a very fast regression-based algorithm, which builds on the densely populated anchored neighborhoods and sublinear search structures. We perform a study of the nature of the features commonly used for SR, observing that those features usually lie in the unitary hypersphere, where every point has a diametrically opposite one, i.e., its antipode, with same module and angle, but the opposite direction. Even though, we validate the benefits of using antipodally invariant metrics, most of the binary splits use Euclidean distance, which does not handle antipodes optimally. In order to benefit from both the worlds, we propose a simple yet effective antipodally invariant transform that can be easily included in the Euclidean distance calculation. We modify the original spherical hashing algorithm with this metric in our antipodally invariant spherical hashing scheme, obtaining the same performance as a pure antipodally invariant metric. We round up our contributions with a novel feature transform that obtains a better coarse approximation of the input image thanks to iterative backprojection. The performance of our method, which we named antipodally invariant SR, improves quality (Peak Signal to Noise Ratio) and it is faster than any other state-of-the-art method.Peer ReviewedPostprint (author's final draft
Unsupervised Video Understanding by Reconciliation of Posture Similarities
Understanding human activity and being able to explain it in detail surpasses
mere action classification by far in both complexity and value. The challenge
is thus to describe an activity on the basis of its most fundamental
constituents, the individual postures and their distinctive transitions.
Supervised learning of such a fine-grained representation based on elementary
poses is very tedious and does not scale. Therefore, we propose a completely
unsupervised deep learning procedure based solely on video sequences, which
starts from scratch without requiring pre-trained networks, predefined body
models, or keypoints. A combinatorial sequence matching algorithm proposes
relations between frames from subsets of the training data, while a CNN is
reconciling the transitivity conflicts of the different subsets to learn a
single concerted pose embedding despite changes in appearance across sequences.
Without any manual annotation, the model learns a structured representation of
postures and their temporal development. The model not only enables retrieval
of similar postures but also temporal super-resolution. Additionally, based on
a recurrent formulation, next frames can be synthesized.Comment: Accepted by ICCV 201
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