Reproducible research is the bedrock of experimental science. To enable the deployment of large-scale proteomics, we assess the reproducibility of mass spectrometry (MS) over time and across instruments and develop computational methods for improving quantitative accuracy. We perform 1560 data independent acquisition (DIA)-MS runs of eight samples containing known proportions of ovarian and prostate cancer tissue and yeast, or control HEK293T cells. Replicates are run on six mass spectrometers operating continuously with varying maintenance schedules over four months, interspersed with~5000 other runs. We utilise negative controls and replicates to remove unwanted variation and enhance biological signal, outperforming existing methods. We also design a method for reducing missing values. Integrating these computational modules into a pipeline (ProNorM), we mitigate variation among instruments over time and accurately predict tissue proportions. We demonstrate how to improve the quantitative analysis of large-scale DIA-MS data, providing a pathway toward clinical proteomics.
This paper considers near-real time detection of beetle infestation in North American pine forests using MODIS 8-days 500 m data. Two methods are considered, both using a single time series for detection of beetle infestation by analyzing the statistics of the trend component of the signal. The first method estimates the trend component of the vegetation index time series by fitting an underlying triply modulated cosine model over a sliding window, using nonlinear least squares (NLS), and the second method uses a -point moving average finite impulse response (FIR) filter. Both the methods perform well and show similar performance on simulated datasets. The methods are also tested on many difference and ratio-indices of a real-world dataset with change and no-change examples taken from the Rocky Mountain region of the United States and of British Columbia in Canada. The results suggest that both the methods detect beetle infestation reliably in almost all the vegetation index datasets. However, the model-based method (NLSbased) performs better in terms of the detection delay. Red Green Index (RGI), when used with the model-based method, provides the best tradeoff between the detection delay and accuracy. Furthermore, 90%, 50%, and 25% cross-validations are also performed for the threshold selection on RGI dataset, and it is shown that the selected threshold works well on the test data. In the end, it is also shown that the model-based method outperforms a recently published method for near-real time disturbance detection in MODIS data, in both accuracy and detection delay.
To improve statistical approaches for near real-time land cover change detection in nonGaussian time-series data, we propose a supervised land cover change detection framework in which a MODIS NDVI time series is modeled as a triply modulated cosine function using the extended Kalman filter and the trend parameter of the triply modulated cosine function is used to derive repeated sequential probability ratio test (RSPRT) statistics. The statistics are based on relative density ratios estimated directly from the training set by a relative unconstrained least squares importance Fitting (RULSIF) algorithm, unlike traditional likelihood ratio-based test statistics. We test the framework on simulated, synthetic, and real-world beetle infestation datasets, and show that using estimated relative density ratios, instead of assuming the individual density functions to be Gaussian or approximating them with Gaussian Kernels, in the RSPRT statistics achieves better performance in terms of accuracy and detection delay. We verify the efficiency of the proposed approach by comparing its performance with three existing methods on all the three datasets under consideration in this study. We also propose a simple heuristic technique that tunes the threshold efficiently in difficult cases of near real-time change detection, when we need to take three performance indices, namely, false positives, false negatives, and mean detection delay, into account simultaneously. Index Terms-Change detection, extended Kalman filter (EKF), model fitting, MODIS, relative density ratio, time series.
I. INTRODUCTIONL AND COVER change detection research has seen significant recent contributions [1]- [19]. However, every proposed framework has its limitations on global scale due to particularity of the task at hand and the circumstances under which it is developed. Hence, no single framework is optimal in a wide range of scenarios simultaneously. Therefore, an efficient change detection framework is always in demand for a particular task and circumstances under consideration. In this Manuscript
The paper considers the detection of beetle infestations in North American pine forests using high temporal resolution, coarse spatial resolution MODIS remotely sensed satellite images. Two methods are proposed to detect beetle infestation, both applying a triply modulated cosine model. The first method uses an Extended Kalman Filter (EKF) for estimating model parameters, and the second a Least Squares estimator. When beetles infest a forest, the changes in the affect large geographical area. Therefore, the change detection metrics are based on the time series of each pixel, and do not utilize information from neighboring pixels. Using data from the Rocky Mountain region of the United States and of British Columbia in Canada, we show that our methods are highly effective at detecting beetle infestations.
This paper assesses the performance of DoTRules-a dictionary of trusted rules-as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of multiple rule sets with rules constructed based on different class frequencies and sequences of occurrences. Shannon entropy was derived for assessing the uncertainty of every rule and the subsequent filtering of unreliable rules. DoTRules is not only a transparent approach for image classification but also a tool to map rule uncertainty, where rule uncertainty assessment can be applied as an estimate of classification accuracy prior to image classification. In this research, the proposed image classification framework is implemented using three world reference hyperspectral image datasets. We found that the overall accuracy of classification using the proposed ensemble framework was superior to state-of-the-art ensemble algorithms, as well as two non-ensemble algorithms, at multiple training sample sizes. We believe DoTRules can be applied more generally to the classification of discrete data such as hyperspectral satellite imagery products.Bayesian [17], maximum margin [18], evolutionary [19], clustering [9], association rule learning [20], artificial neural network [12,21,22] and deep learning [23] methods (see Figure 1). Regardless of the classification performance, many of these algorithms act as black-boxes, resulting in a poor recognition of the classification structure and robustness owing to the high-dimensionality of the data [24,25].
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