Constant Window Size Research Articles

Dynamic Pooling Improves Nanopore Base Calling Accuracy.

In nanopore sequencing, electrical signal is measured as DNA molecules pass through the sequencing pores. Translating these signals into DNA bases (base calling) is a highly non-trivial task, and its quality has a large impact on the sequencing accuracy. The most successful nanopore base callers to date use convolutional neural networks (CNN) to accomplish the task. Convolutional layers in CNNs are typically composed of filters with constant window size, performing best in analysis of signals with uniform speed. However, the speed of nanopore sequencing varies greatly both within reads and between sequencing runs. Here, we present dynamic pooling, a novel neural network component, which addresses this problem by adaptively adjusting the pooling ratio. To demonstrate the usefulness of dynamic pooling, we developed two base callers: Heron and Osprey. Heron improves the accuracy beyond the experimental high-accuracy base caller Bonito developed by Oxford Nanopore. Osprey is a fast base caller that can compete in accuracy with Guppy high-accuracy mode, but does not require GPU acceleration and achieves a near real-time speed on common desktop CPUs. Availability: https://github.com/fmfi-compbio/osprey, https://github.com/fmfi-compbio/heron.

Parametric Dependencies of Sliding Window Correlation.

In this paper, we explore the dependence of sliding window correlation (SWC) results on different parameters of correlating signals. The SWC is extensively used to explore the dynamics of functional connectivity (FC) networks using resting-state functional MRI (rsfMRI) scans. These scanned signals often contain multiple amplitudes, frequencies, and phases. However, the exact values of these parameters are unknown. Two recent studies explored the relationship of window length and frequencies (minimum/maximum) in the correlating signals. We extend the findings of these studies by using two deterministic signals with multiple amplitudes, frequencies, and phases. Afterward, we modulate one of the signals to introduce dynamics (nonstationarity) in their relationship. We also explore the relationship of window length and frequency band for real rsfMRI data. For deterministic signals, the spurious fluctuations due to the method itself minimize, and the SWC estimates the stationary correlation when frequencies in the signals have specific relationship. For dynamic relationship also, the undesirable frequencies were removed under specific conditions for the frequencies. For real rsfMRI data, the SWC results varied with frequencies and window length. In the absence of any "ground truth" for different parameters in real rsfMRI signals, the SWC with a constant window size may not be a reliable method to study the dynamics of the FC. This study reveals the parametric dependencies of the SWC and its limitation as a method to analyze dynamics of FC networks in the absence of any ground truth.

SigWinR; the SigWin-detector updated and ported to R

BackgroundOur SigWin-detector discovers significantly enriched windows of (genomic) elements in any sequence of values (genes or other genomic elements in a DNA sequence) in a fast and reproducible way. However, since it is grid based, only (life) scientists with access to the grid can use this tool. Therefore and on request, we have developed the SigWinR package which makes the SigWin-detector available to a much wider audience. At the same time, we have introduced several improvements to its algorithm as well as its functionality, based on the feedback of SigWin-detector end users.FindingsTo allow usage of the SigWin-detector on a desktop computer, we have rewritten it as a package for R: SigWinR. R is a free and widely used multi platform software environment for statistical computing and graphics. The package can be installed and used on all platforms for which R is available. The improvements involve: a visualization of the input-sequence values supporting the interpretation of Ridgeograms; a visualization allowing for an easy interpretation of enriched or depleted regions in the sequence using windows of pre-defined size; an option that allows the analysis of circular sequences, which results in rectangular Ridgeograms; an application to identify regions of co-altered gene expression (ROCAGEs) with a real-life biological use-case; adaptation of the algorithm to allow analysis of non-regularly sampled data using a constant window size in physical space without resampling the data. To achieve this, support for analysis of windows with an even number of elements was added.ConclusionBy porting the SigWin-detector as an R package, SigWinR, improving its algorithm and functionality combined with adequate performance, we have made SigWin-detector more useful as well as more easily accessible to scientists without a grid infrastructure.

Model-based light stripe detection for indoor navigation

Light stripe projection (LSP) is one of the most robust 3D recognition methods, and the general method of light stripe feature (LSF) detection is Laplacian of Gaussian (LOG) filtering. If distances to objects are various, as in the case of indoor navigation, LSF width becomes various according to distance. As the window size of spatial filtering influences the performance significantly, various LSF widths disturb LOG-based LSF detection with constant base length, that is constant window size. The irradiance maps of LSFs were reconstructed by high dynamic range imaging (HDRi) while changing the distance. By analyzing the irradiance maps, LSF irradiance map was modeled as a 2D Gaussian function whose parameters were approximated as functions of distance. After deriving LSF width function of distance, it was transformed into LSF width function of pixel coordinates by one-to-one relation between distance and y coordinates in LSP. The LSF width function can provide proper base length to LOG filtering. A self-calibration procedure is proposed which can estimate the parameters of LSF width function with only several images captured at new environment. Experimental results show that proposed model-based LSF detection overcomes normal LOG-based LSF detection.

Corner detection using iterative Gaussian smoothing with constant window size

In contrast to the existing Gaussian smoothing process with varying window size, an iterative Gaussian convolution with constant window size is proposed. The iterative process is shown to converge as the norm of the convolution matrix is less than unity. For a closed digital curve the number of iterations is shown to be related to the number of points on the curve. The Gaussian filter coefficients that are used to smooth the curve are shown to enjoy the scale-space property. A scale-space map showing the location of the maxima of absolute curvature over iterations is proposed. The map is converted into a tree organization on the basis of an analysis of the scale-space behavior of different corner models such as Γ models, END models and STAIR models. Corners are detected in a process of interpreting the tree. The corner detector has been applied successfully on different digital curves even in presence of additive white Gaussian noise and at varying orientations.