Feature Adaptation Predictive Coding for Quantized Block Compressive Sensing of COVID-19 X-Ray Images

With the development of remote X-ray detection for Corona Virus Disease 2019 (COVID-19), the quantized block compressive sensing technology plays an important role when remotely acquiring the chest X-ray images of COVID-19 infected people and significantly promoting the portable telemedicine imaging applications. In order to improve the encoding performance of quantized block compressive sensing, a feature adaptation predictive coding (FAPC) method is proposed for the remote transmission of COVID-19 X-ray images. The proposed FAPC method can adaptively calculate the block-wise prediction coefficients according to the main features of COVID-19 X-ray images, and thus provide the optimal prediction candidate from the feature-guided candidate set. The proposed method can implement the high-efficiency encoding of X-ray images, and then swiftly transmit the telemedicine-oriented chest images. The experimental results show that compared with the state-of-the-art predictive coding methods, both rate-distortion and complexity performance of our FAPC method have enough competitive advantages. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Group Testing Matrix Design for PCR Screening with Real-Valued Measurements.

Single-step nonadaptive group testing approaches for reducing the number of tests required to detect a small subset of positive samples from a larger set require solving two algorithmic problems. First, how to design the samples-to-tests measurement matrix, and second, how to decode the results of the tests to uncover positive samples. In this study, we focus on the first challenge. We introduce real-valued group testing, which matches the characteristics of existing PCR testing pipelines more closely than combinatorial group testing or compressed sensing settings. We show a set of conditions that allow measurement matrices to guarantee unambiguous decoding of positives in this new setting. For small matrix sizes, we also propose an algorithm for constructing matrices that meet the proposed condition. On simulated data sets, we show that the matrices resulting from the algorithm can successfully recover positive samples at higher positivity rates than matrices designed for combinatorial group testing setting. We use wet laboratory experiments involving SARS-CoV-2 nasopharyngeal swab samples to further validate the approach.

Recovery Algorithms for Pooled RT-qPCR based Covid-19 Screening

We consider the problem of sparse signal recovery in a non-adaptive pool-test setting using quantitative measurements from a non-linear model. The quantitative measurements are obtained using the reverse transcription (quantitative) polymerase chain reaction (RT-qPCR) test, which is the standard test used to detect Covid-19. Each quantitative measurement refers to the cycle threshold, a proxy for the viral load in the test sample. We propose two novel, robust recovery algorithms based on alternating direction method of multipliers and block coordinate descent to recover the individual sample cycle thresholds and hence determine the sick individuals, given the pooled sample cycle thresholds and the pooling matrix. We numerically evaluate the normalized mean squared error, false positive rate, false negative rate, and the maximum sparsity levels up to which error-free recovery is possible. We also demonstrate the advantage of using quantitative measurements (as opposed to binary outcomes) in non-adaptive pool testing methods in terms of the testing rate using publicly available data on Covid-19 testing. The simulation results show the effectiveness of the proposed algorithms. IEEE

Blending camera and 77 GHz radar sensing for equitable, robust plethysmography

With the resurgence of non-contact vital sign sensing due to the COVID-19 pandemic, remote heart-rate monitoring has gained significant prominence. Many existing methods use cameras;however previous work shows a performance loss for darker skin tones. In this paper, we show through light transport analysis that the camera modality is fundamentally biased against darker skin tones. We propose to reduce this bias through multi-modal fusion with a complementary and fairer modality - radar. Through a novel debiasing oriented fusion framework, we achieve performance gains over all tested baselines and achieve skin tone fairness improvements over the RGB modality. That is, the associated Pareto frontier between performance and fairness is improved when compared to the RGB modality. In addition, performance improvements are obtained over the radar-based method, with small trade-offs in fairness. We also open-source the largest multi-modal remote heart-rate estimation dataset of paired camera and radar measurements with a focus on skin tone representation. © 2022 Owner/Author.

DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19.

Given that the usual process of developing a new vaccine or drug for COVID-19 demands significant time and funds, drug repositioning has emerged as a promising therapeutic strategy. We propose a method named DRPADC to predict novel drug-disease associations effectively from the original sparse drug-disease association adjacency matrix. Specifically, DRPADC processes the original association matrix with the WKNKN algorithm to reduce its sparsity. Furthermore, multiple types of similarity information are fused by a CKA-MKL algorithm. Finally, a compressed sensing algorithm is used to predict the potential drug-disease (virus) association scores. Experimental results show that DRPADC has superior performance than several competitive methods in terms of AUC values and case studies. DRPADC achieved the AUC value of 0.941, 0.955 and 0.876 in Fdataset, Cdataset and HDVD dataset, respectively. In addition, the conducted case studies of COVID-19 show that DRPADC can predict drug candidates accurately.

Real-Valued Group Testing for Quantitative Molecular Assays

Combinatorial group testing and compressed sensing both focus on recovering a sparse vector of dimensionality n from a much smaller number m< n of measurements. In the first approach, the problem is defined over the Boolean field – the goal is to recover a Boolean vector and measurements are Boolean;in the second approach, the unknown vector and the measurements are over the reals. Here, we focus on real-valued group testing setting that more closely fits modern testing protocols relying on quantitative measurements, such as qPCR, where the goal is recovery of a sparse, Boolean vector and the pooling matrix needs to be Boolean and sparse, but the unknown input signal vector and the measurement outcomes are nonnegative reals, and the matrix algebra implied in the test protocol is over the reals. With the recent renewed interest in group testing, focus has been on quantitative measurements resulting from qPCR, but the method proposed for sample pooling were based on matrices designed with Boolean measurements in mind. Here, we investigate constructing pooling matrices dedicated for the real-valued group testing. We provide conditions for pooling matrices to guarantee unambiguous decoding of positives in this setting. We also show a deterministic algorithm for constructing matrices meeting the proposed condition, for small matrix sizes that can be implemented using a laboratory robot. Using simulated data, we show that the proposed approach leads to matrices that can be applied for higher positivity rates than combinatorial group testing matrices considered for viral testing previously. We also validate the approach through wet lab experiments involving SARS-CoV-2 nasopharyngeal swab samples. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection.

We propose 'Tapestry', a single-round pooled testing method with application to COVID-19 testing using quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR) that can result in shorter testing time and conservation of reagents and testing kits, at clinically acceptable false positive or false negative rates. Tapestry combines ideas from compressed sensing and combinatorial group testing to create a new kind of algorithm that is very effective in deconvoluting pooled tests. Unlike Boolean group testing algorithms, the input is a quantitative readout from each test and the output is a list of viral loads for each sample relative to the pool with the highest viral load. For guaranteed recovery of [Formula: see text] infected samples out of [Formula: see text] being tested, Tapestry needs only [Formula: see text] tests with high probability, using random binary pooling matrices. However, we propose deterministic binary pooling matrices based on combinatorial design ideas of Kirkman Triple Systems, which balance between good reconstruction properties and matrix sparsity for ease of pooling while requiring fewer tests in practice. This enables large savings using Tapestry at low prevalence rates while maintaining viability at prevalence rates as high as 9.5%. Empirically we find that single-round Tapestry pooling improves over two-round Dorfman pooling by almost a factor of 2 in the number of tests required. We evaluate Tapestry in simulations with synthetic data obtained using a novel noise model for RT-PCR, and validate it in wet lab experiments with oligomers in quantitative RT-PCR assays. Lastly, we describe use-case scenarios for deployment.