1,425 research outputs found
Prediction of amyloid fibril-forming segments based on a support vector machine
<p>Abstract</p> <p>Background</p> <p>Amyloid fibrillar aggregates of proteins or polypeptides are known to be associated with many human diseases. Recent studies suggest that short protein regions trigger this aggregation. Thus, identifying these short peptides is critical for understanding diseases and finding potential therapeutic targets.</p> <p>Results</p> <p>We propose a method, named Pafig (Prediction of amyloid fibril-forming segments) based on support vector machines, to identify the hexpeptides associated with amyloid fibrillar aggregates. The features of Pafig were obtained by a two-round selection from AAindex. Using a 10-fold cross validation test on Hexpepset dataset, Pafig performed well with regards to overall accuracy of 81% and Matthews correlation coefficient of 0.63. Pafig was used to predict the potential fibril-forming hexpeptides in all of the 64,000,000 hexpeptides. As a result, approximately 5.08% of hexpeptides showed a high aggregation propensity. In the predicted fibril-forming hexpeptides, the amino acids – alanine, phenylalanine, isoleucine, leucine and valine occurred at the higher frequencies and the amino acids – aspartic acid, glutamic acid, histidine, lysine, arginine and praline, appeared with lower frequencies.</p> <p>Conclusion</p> <p>The performance of Pafig indicates that it is a powerful tool for identifying the hexpeptides associated with fibrillar aggregates and will be useful for large-scale analysis of proteomic data.</p
Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines
<p>Abstract</p> <p>Background</p> <p>Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occur approximately every 1000 bases in the overall human population. The non-synonymous SNPs (nsSNPs) that lead to amino acid changes in the protein product may account for nearly half of the known genetic variations linked to inherited human diseases. One of the key problems of medical genetics today is to identify nsSNPs that underlie disease-related phenotypes in humans. As such, the development of computational tools that can identify such nsSNPs would enhance our understanding of genetic diseases and help predict the disease.</p> <p>Results</p> <p>We propose a method, named Parepro (Predicting the amino acid replacement probability), to identify nsSNPs having either deleterious or neutral effects on the resulting protein function. Two independent datasets, HumVar and NewHumVar, taken from the PhD-SNP server, were applied to train the model and test the robustness of Parepro. Using a 20-fold cross validation test on the HumVar dataset, Parepro achieved a Matthews correlation coefficient (MCC) of 50% and an overall accuracy (Q2) of 76%, both of which were higher than those predicted by the methods, such as PolyPhen, SIFT, and HydridMeth. Further analysis on an additional dataset (NewHumVar) using Parepro yielded similar results.</p> <p>Conclusion</p> <p>The performance of Parepro indicates that it is a powerful tool for predicting the effect of nsSNPs on protein function and would be useful for large-scale analysis of genomic nsSNP data.</p
Device modeling of superconductor transition edge sensors based on the two-fluid theory
In order to support the design and study of sophisticated large scale
transition edge sensor (TES) circuits, we use basic SPICE elements to develop
device models for TESs based on the superfluid-normal fluid theory. In contrast
to previous studies, our device model is not limited to small signal
simulation, and it relies only on device parameters that have clear physical
meaning and can be easily measured. We integrate the device models in design
kits based on powerful EDA tools such as CADENCE and OrCAD, and use them for
versatile simulations of TES circuits. Comparing our simulation results with
published experimental data, we find good agreement which suggests that device
models based on the two-fluid theory can be used to predict the behavior of TES
circuits reliably and hence they are valuable for assisting the design of
sophisticated TES circuits.Comment: 10pages,11figures. Accepted to IEEE Trans. Appl. Supercon
Coefficient of earth pressure at rest of a saturated artificially mixed soil from oedometer tests
AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization
Neural Radiance Fields (NeRF) have shown promise in generating realistic
novel views from sparse scene images. However, existing NeRF approaches often
encounter challenges due to the lack of explicit 3D supervision and imprecise
camera poses, resulting in suboptimal outcomes. To tackle these issues, we
propose AltNeRF -- a novel framework designed to create resilient NeRF
representations using self-supervised monocular depth estimation (SMDE) from
monocular videos, without relying on known camera poses. SMDE in AltNeRF
masterfully learns depth and pose priors to regulate NeRF training. The depth
prior enriches NeRF's capacity for precise scene geometry depiction, while the
pose prior provides a robust starting point for subsequent pose refinement.
Moreover, we introduce an alternating algorithm that harmoniously melds NeRF
outputs into SMDE through a consistence-driven mechanism, thus enhancing the
integrity of depth priors. This alternation empowers AltNeRF to progressively
refine NeRF representations, yielding the synthesis of realistic novel views.
Additionally, we curate a distinctive dataset comprising indoor videos captured
via mobile devices. Extensive experiments showcase the compelling capabilities
of AltNeRF in generating high-fidelity and robust novel views that closely
resemble reality
A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image Restoration
Inspired by the fact that the matrix formulated by nonlocal similar patches
in a natural image is of low rank, the rank approximation issue have been
extensively investigated over the past decades, among which weighted nuclear
norm minimization (WNNM) and weighted Schatten -norm minimization (WSNM) are
two prevailing methods have shown great superiority in various image
restoration (IR) problems. Due to the physical characteristic of color images,
color image restoration (CIR) is often a much more difficult task than its
grayscale image counterpart. However, when applied to CIR, the traditional
WNNM/WSNM method only processes three color channels individually and fails to
consider their cross-channel correlations. Very recently, a quaternion-based
WNNM approach (QWNNM) has been developed to mitigate this issue, which is
capable of representing the color image as a whole in the quaternion domain and
preserving the inherent correlation among the three color channels. Despite its
empirical success, unfortunately, the convergence behavior of QWNNM has not
been strictly studied yet. In this paper, on the one side, we extend the WSNM
into quaternion domain and correspondingly propose a novel quaternion-based
WSNM model (QWSNM) for tackling the CIR problems. Extensive experiments on two
representative CIR tasks, including color image denoising and deblurring,
demonstrate that the proposed QWSNM method performs favorably against many
state-of-the-art alternatives, in both quantitative and qualitative
evaluations. On the other side, more importantly, we preliminarily provide a
theoretical convergence analysis, that is, by modifying the quaternion
alternating direction method of multipliers (QADMM) through a simple
continuation strategy, we theoretically prove that both the solution sequences
generated by the QWNNM and QWSNM have fixed-point convergence guarantees.Comment: 46 pages, 10 figures; references adde
An Improved Timing Attack with Error Detection on RSA-CRT
Several types of timing attacks have been published, but they are either in theory or hard to be taken into practice. In order to improve the feasibility of attack, this paper proposes an advance timing attack scheme on RSA-CRT with T-test statistical tool. Similar timing attacks have been presented, such as BB-Attack and Shindler’s attack, however none of them applied statistical tool in their methods with such efficiency, and showed the complete recovery in practice by attacking on RSA-CRT. With T-test, we enlarge the 0-1 gap, reduce the neighborhood size and improve the precision of decision. However, the most contribution of this paper is that our algorithm has an error detection property which can detect the erroneous decision of guessing qk and correct it. We could make the success rate of recovering q to be 100% indeed for interprocess timing attack, recovery 1024bits RSA key completely in practice
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