Signal Detection Method Research Articles

Assessment of adverse events of the novel cardiovascular drug vericiguat: a real-world pharmacovigilance study based on FAERS

ABSTRACT Background This study aims to analyze the adverse event reports (AERs) to vericiguat using data from the Food and Drug Administration Adverse Event Reporting System (FAERS) and provide evidence for the clinical use. Methods AERs due to vericiguat from 2021Q1 to 2024Q1 identified as the primary suspect were screened, with duplicate reports subsequently eliminated. Various quantitative signal detection methods, including reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network, and multi-item gamma poisson shrinker, were then employed for data mining and analysis. Signal strength is represented by the 95% confidence interval, information component (IC), and empirical Bayesian geometric mean (EBGM). Results A total of 617 vericiguat-related AERs were identified. Strong signals were observed in 21 system organ classes. Furthermore, the most frequently reported preferred terms (PT) was hypotension (n = 86, ROR 25.92, PRR 24.11, IC 4.59, EBGM 24.07), followed by dizziness (n = 52, ROR 6.44, PRR 6.20, IC 2.63, EBGM 6.20), malaise (n = 25, ROR 3.59, PRR 3.54, IC 1.82, EBGM 3.54), blood pressure decreased (n = 23, ROR 20.00, PRR 19.64, IC 4.29, EBGM 19.61), and anemia (n = 21, ROR 6.67, PRR 6.57, IC 2.72, EBGM 6.57). Conclusions This study extended the adverse reactions documented in the FDA instruction and provided supplementary evidence regarding the clinical safety of vericiguat.

Optimal Significance Levels and Sample Sizes for Signal Detection Methods Based on Non-constant Hazards.

Statistical methods for signal detection of adverse drug reactions (ADRs) in electronic health records (EHRs) need information about optimal significance levels and sample sizes to achieve sufficient power. Sauzet and Cornelius proposed tests for signal detection based on the hazard functions of Weibull type distributions (WSP tests) which use the time-to-event information available in EHRs. Optimal significance levels and sample sizes for the application of the WPS tests are derived. A simulation study was performed with a range of scenarios for sample size, rate of event due (ADRs), and not due to the drug and random time to ADR occurrence. Based on the area under the curve of the receiver operating characteristic graph, we obtain optimal significance levels of the different WSP tests for the implementation in a hypothesis free signal detection setting and approximate sample sizes required to reach a power of 80% or 90%. The dWSP-pPWSP (combination of double WSP and power WSP) test with a significance level of 0.004 was recommended. Sample sizes needed for a power of 80% were found to start at 60 events for an ADR rate equal to the background rate of 0.1. The number of events required for a background rate of 0.05 and an ADR rate equal to a 20% increase of the background rate was 900. Based on this study, it is recommended to use the dWSP-pWSP test combination for signal detection with a significance level of 0.004 when the same test is applied to all adverse events not depending on rates.

A Deep Learning-Based Emergency Alert Wake-Up Signal Detection Method for the UHD Broadcasting System.

With the increasing frequency and severity of disasters and accidents, there is a growing need for efficient emergency alert systems. The ultra-high definition (UHD) broadcasting service based on Advanced Television Systems Committee (ATSC) 3.0, a leading terrestrial digital broadcasting system, offers such capabilities, including a wake-up function for minimizing damage through early alerts. In case of a disaster situation, the emergency alert wake-up signal is transmitted, allowing UHD TVs to be activated, enabling individuals to receive emergency alerts and access emergency broadcasting content. However, conventional methods for detecting the bootstrap signal, essential for this function, typically require an ATSC 3.0 demodulator. In this paper, we propose a novel deep learning-based method capable of detecting an emergency wake-up signal without the need for an ATSC 3.0. The proposed method leverages deep learning techniques, specifically a deep neural network (DNN) structure for bootstrap detection and a convolutional neural network (CNN) structure for wake-up signal demodulation and to detect the bootstrap and 2 bit emergency alert wake-up signal. Specifically, our method eliminates the need for Fast Fourier Transform (FFT), frequency synchronization, and interleaving processes typically required by a demodulator. By applying a deep learning in the time domain, we simplify the detection process, allowing for the detection of an emergency alert signal without the full suite of demodulator components required for ATSC 3.0. Furthermore, we have verified the performance of the deep learning-based method using ATSC 3.0-based RF signals and a commercial Software-Defined Radio (SDR) platform in a real environment.

Weak signal detection based on pulse width counting method for underwater wireless optical communication with an analog mode PMT detector

This work proposes a weak signal detection method based on pulse width counting (PWC) for the on-off keying (OOK) underwater wireless optical communication (UWOC) system with an analog mode photomultiplier tube (PMT) detector. The signal output model of the analog mode PMT in weak light communication and the influence of pulse overlap are investigated. We experimentally evaluate the proposed algorithm under different sampling rates, detection thresholds, data rates as well as received optical powers (ROPs), and compare the performance of the proposed approach with that of pulse amplitude detection and pulse peak counting. A 10-Mbps OOK UWOC link is realized with a sensitivity of -71.5 dBm in a 7-meter tank, which is 1.1-dB and 3.8-dB lower than that of pulse peak counting (PPC) and pulse amplitude detection (PAD) methods, respectively, and the total link attenuation is 94.8 dB. This system utilizes the analog mode PMT with larger dynamic range than photon-counting mode PMT to achieve weak light signal detection, which benefits design long-range UWOC systems.

Data model extraction - a new method of signal detection for multi-level index modulation

Instead of treating the data as no model or a fixed model, can we detect the data through modeling it dynamically for wireless communications? In this paper, a new method, data model extraction, for signal detection is studied for multi-level index modulation (MTL-IM) system. The reason why MTL-IM is investigated is based on its natural attributes that the information symbols are divided into explicit categories by distinct energy. A one-size-fits-all continuous model rather than a discrete model is used to describe the data. In this way, the problem of estimation of the discrete values of the energy level is transformed into the problem of estimation of both the constructed model itself and its parameters. Subsequently, the optimization function is built over the constructed model of the data. By this means, a mechanism of picking up information through model matching is established. Also, a new data-model-extraction-assisted adaptive message delay passing algorithm, in which the learning rate is introduced to delay the message from the adjacent nodes, is investigated. Simulation results demonstrate the effectiveness of the data model extraction method and the proposed solution.

Power System Signal-Detection Method Based on the Accelerated Unsaturated Stochastic Resonance Principle

The classical bistable stochastic resonance algorithm has an inherent output saturation defect that restricts the amplitude of the output signal. This paper examines the causes of this phenomenon and its negative impact on the detection of weak signals. Proposing the Unsaturated Bistable Stochastic Resonance (UBSR) detection algorithm involves constructing a segmented potential function using a linear function to eliminate the effect of higher-order terms in the classical stochastic resonance algorithm. A new type of segmented potential function has been created by combining exponential and linear functions. This new function helps to eliminate the impact of higher-order terms in classical algorithms while also improving the noise immunity of the stochastic resonance system. This results in the development of the accelerated stochastic resonance (ASR) detection algorithm. In this paper, the Kramers escape rate and output signal-to-noise ratio of two improved stochastic resonance algorithms are theoretically derived and compared with the classical bistable stochastic resonance algorithms, and the proposed algorithms are able to effectively avoid the output saturation phenomenon and have more excellent detection performance under strong background noise.

Sparse Reconstruction-Based Joint Signal Processing for MIMO-OFDM-IM Integrated Radar and Communication Systems

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) technology is widely used in integrated radar and communication systems (IRCSs). Moreover, index modulation (IM) is a reliable OFDM transmission scheme in the field of communication, which transmits information by arranging several distinguishable constellations. In this paper, we propose a sparse reconstruction-based joint signal processing scheme for integrated MIMO-OFDM-IM systems. Combining the advantages of MIMO and OFDM-IM technologies, the integrated MIMO-OFDM-IM signal design is realized through the reasonable allocation of bits and subcarriers, resulting in better intercarrier interference (ICI) resistance and a higher transmission efficiency. Taking advantage of the sparseness of OFDM-IM, an improved target parameter estimation method based on sparse signal reconstruction is explored to eliminate the influence of empty subcarriers on the matched filtering at the receiver side. In addition, an improved sequential Monte Carlo signal detection method is introduced to realize the efficient detection of communication signals. The simulation results show that the proposed integrated system is 5 dB lower in the peak sidelobe ratio (PSLR) and 1.5 ×105 lower in the number of complex multiplications than the latest MIMO-OFDM system and can achieve almost the same parameter estimation performance. With the same spectral efficiency, it has a lower bit error rate (BER) than existing methods.

A novel underwater weak signal detection method based on parameter optimized VMD and 3D chaotic system

Based on chaos theory, combined with improved variational mode decomposition, a novel method for underwater weak signal detection and frequency extraction is proposed. The aim of this research is to address the challenge of detecting underwater signals and extracting their frequencies under ultra-low Signal-to-Noise Ratio (SNR). A novel three-dimensional (3D) chaotic system with infinitely shrinking attractors is proposed, which incorporates a cosine function to achieve chaos control and enhancement. The nonlinear characteristics of the system, such as Lyapunov exponent and bifurcation, are analyzed in detail. Then, we design a frequency array and perform scale transformation on the chaotic system to construct a detection model that covering all frequencies. The model can transition from critical chaos to large period or intermittent chaos in the presence of external periodic signals by adjusting the parameters of the cosine function. Considering the relationship between the generation of intermittent chaos and the frequency of target signal, a frequency extraction algorithm based on chaos sparrow search algorithm optimization variational mode decomposition (CSSA-VMD) and Hilbert transform is designed. The algorithm can estimate the real frequency of underwater signals by performing noise reduction and envelope analysis on intermittent chaotic signals. The experimental results demonstrate that the proposed system can detect weak signals in complex marine backgrounds and extract their frequency features, with a detection SNR of -48 dB and a frequency error of less than 0.01%. Importantly, the circuit of the detection system has been implemented, verifying its feasibility for detecting and tracking underwater signals.

Weak signal detection method based on nonlinear differential equations

With the rapid development of computer network technology, it is often necessary to collect weak signals to collect favorable information. The development of signal detection technology is ongoing; however, various issues arise during the detection process. These issues include low efficiency and a high signal noise threshold. However, many problems will be encountered in the process of detection. In order to solve these problems, the nonlinear chaos theory is introduced to detect signals, and the simulation experiments of weak pulse signals and weak partial discharge signals are carried out respectively. The experimental results showed that the detection effect was remarkable in the quasi periodic state, and it had a good detection effect for weak pulse signals. At a signal-to-noise ratio of -25 dB, double coupling system, two-way ring coupling system, and single ring coupling system displayed detection success rates exceeding 98%. Meanwhile, the detection success rate of the strong coupling system was only 12%. Even at a noise signal ratio as low as -40 dB, the dual coupling system still maintained a detection success rate above 80%. The simulation results of partial discharge signal detection showed that there was a high fluctuation only at 2 ms, and the rest was basically stable at about 0 V, indicating that the system had a strong suppression effect on Gaussian white noise. When comparing the simulation results of the detection of the new chaotic system and the double coupling system, it was found that the new chaotic system has a superior impact in detecting weakly attenuated partial discharge signals. Through analysis of the system’s dynamic behavior, the research confirms its rich dynamic characteristics and sheds light on the reasons for phase state mutation and missed detection. The noise system is utilized for comparing the performance of various systems, with the goal of enhancing the system’s detection capability.

A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches.

The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to ensure the safety profile of a drug product. Machine learning (ML) has the potential to assist with signal detection and supplement traditional pharmacovigilance (PV) surveillance methods. This pilot ML modeling study was designed to detect potential safety signals for two AbbVie products and test the model's capability of detecting safety signals earlier than humans. Drug X, a mature product with post-marketing data, and Drug Y, a recently approved drug in another therapeutic area, were selected. Gradient boosting-based ML approaches (e.g., XGBoost) were applied as the main modeling strategy. For Drug X, eight true signals were present in the test set. Among 12 potential new signals generated, four were true signals with a 50.0% sensitivity rate and a 33.3% positive predictive value (PPV) rate. Among the remaining eight potential new signals, one was confirmed as a signal and detected six months earlier than humans. For Drug Y, nine true signals were present in the test set. Among 13 potential new signals generated, five were true signals with a 55.6% sensitivity rate and a 38.5% PPV rate. Among the remaining eight potential new signals, none were confirmed as true signals upon human review. This model demonstrated acceptable accuracy for safety signal detection and potential for earlier detection when compared to humans. Expert judgment, flexibility, and critical thinking are essential human skills required for the final, accurate assessment of adverse event cases.

Research on an Alpha Navigation Signal Detection Method Based on Multichannel Orthogonal Correlation

The Alpha navigation system is the only operating radio system based on very-low-frequency (VLF) signals that can be used to research VLF navigation, timing, and ionospheric characteristics. The detection of the Alpha navigation signal is the key step in the Alpha receiver; however, the received Alpha navigation signal is susceptible to noise and mutual interference, which deteriorates signal detection performance. This paper presents a multichannel orthogonal correlation method for Alpha navigation signal detection. Once the three frequency signals of the Alpha navigation system are obtained using a notch filter, station identification is realized using a multichannel orthogonal correlation method and signal format. The selection of key parameters and the detection performance under noise and mutual interference are analyzed. This method’s detection probability exceeds 90% when the signal-to-noise ratio (SNR) is greater than −10 dB. The influence of mutual interference on the signal correlation peak is less than 1% when the signal-to-interference ratio (SIR) of the mutual interference is greater than −28 dB. The proposed method is verified using an actual signal collected using an Alpha receiver. The results show that an Alpha signal can be detected at an extremely low SNR. This method has strong practicability and satisfies the application requirements of an Alpha receiver.

Using the Moral Injury and Distress Scale to identify clinically meaningful moral injury.

Despite the proliferation of moral injury studies, a remaining gap is distinguishing moral injury from normative distress following exposure to potentially morally injurious events (PMIEs). Our goal was to leverage mental health and functional measures to identify clinically meaningful and functionally impairing moral injury using the Moral Injury and Distress Scale (MIDS). Participants who endorsed PMIE exposure (N = 645) were drawn from a population-based sample of military veterans, health care workers, and first responders. Using signal detection methods, we identified the optimally efficient MIDS score for detecting clinically significant posttraumatic stress and depressive symptom severity, trauma-related guilt, and functional impairment. The most efficient cut scores across outcomes converged between 24 and 27. We recommend a cut score of 27 given that roughly 70% of participants who screened positive on the MIDS at this threshold reported clinically significant mental health symptoms, and approximately 50% reported severe trauma-related guilt and/or functional impairment. Overall, 10.2% of respondents exposed to a PMIE screened positive for moral injury at this threshold, particularly those who identified as a member of a minoritized racial or ethnic group (17.9%) relative to those who identified as White, non-Hispanic (8.0%), aOR = 2.52, 95% CI [1.45, 4.42]. This is the first known study to establish a cut score indicative of clinically meaningful and impairing moral injury. Such scores may enhance clinicians' abilities to conduct measurement-based moral injury care by enabling them to identify individuals at risk of negative outcomes and better understand risk and protective factors for moral injury.

Tree-temporal scan statistics for safety signal detection in vaccine clinical trials.

The evaluation of safety is critical in all clinical trials. However, the quantitative analysis of safety data in clinical trials poses statistical difficulties because of multiple potentially overlapping endpoints. Tree-temporal scan statistic approaches address this issue and have been widely employed in other data sources, but not to date in clinical trials. We evaluated the performance of three complementary scan statistical methods for routine quantitative safety signal detection: the self-controlled tree-temporal scan (SCTTS), a tree-temporal scan based on group comparison (BGTTS), and a log-rank based tree-temporal scan (LgRTTS). Each method was evaluated using data from two phase III clinical trials, and simulated data (simulation study). In the case study, the reference set was adverse events (AEs) in the Reference Safety Information of the evaluated vaccine. The SCTTS method had higher sensitivity than other methods, and after dose 1 detected 80 true positives (TP) with a positive predictive value (PPV) of 60%. The LgRTTS detected 49 TPs with 69% PPV. The BGTTS had 90% of PPV with 38 TPs. In the simulation study, with simulated reference sets of AEs, the SCTTS method had good sensitivity to detect transient effects. The LgRTTS method showed the best performance for the detection of persistent effects, with high sensitivity and expected probability of type I error. These three methods provide complementary approaches to safety signal detection in clinical trials or across clinical development programmes. All three methods formally adjust for multiple testing of large numbers of overlapping endpoints without being excessively conservative.

An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks

This study proposes the utilization of an optical fiber vibration sensor for detecting the superposition of extremely close frequencies in vibration signals. Integration of deep neural networks (DNN) proves to be meaningful and efficient, eliminating the need for signal analysis methods involving complex mathematical calculations and longer computation times. Simulation results of the proposed model demonstrate the remarkable capability to accurately distinguish frequencies below 1 Hz. This underscores the effectiveness of the proposed image-based vibration signal recognition system embedded in DNN as a streamlined yet highly accurate method for vibration signal detection, applicable across various vibration sensors. Both simulation and experimental evaluations substantiate the practical applicability of this integrated approach, thereby enhancing electric motor vibration monitoring techniques.

Detection of iodixanol-induced allergic reaction signals in Chinese inpatients: a multi-center retrospective database study using prescription sequence symmetry analysis.

This study aimed to explore the signal detection method for allergic reactions induced by inpatient iodixanol injection. A database of 3,719,217 hospitalized patients from 20 large Chinese general hospitals was processed and analyzed using the prescription sequence symmetry analysis (PSSA) method. 126,680 inpatients who used iodixanol and were concurrently treated with anti-allergic drugs were analyzed. In the medical records of these patients, only 32 had documented iodixanol allergies. Statistical analysis identified 22 drugs in 4 categories-calcium preparations, adrenergic/dopaminergic agents, glucocorticoids, and antihistamines-as marker drugs. With time intervals of 3, 7, and 28days, the adjusted sequence ratios (aSRs) for all anti-allergics and the 4 categories were greater than 1. The 7-day aSRs were 2.12 (95% CI: 2.08-2.15), 1.70 (95% CI: 1.68-1.73), 3.85 (95% confidence interval [CI]: 3.75-2.30), 2.30 (95% CI: 2.26-2.35), and 1.95 (95% CI: 1.89-2.02), respectively. The proportions of adverse drug events indicated by each signal were as follows: all anti-allergics (2.92%-3%), calcium gluconate (0.19%-0.52%), adrenergic/dopaminergic agents (2.20%-3.37%), glucocorticoids (3.13%-3.76%), and antihistamines (1.05%-1.32%). This first multi-center Chinese inpatient database study detected iodixanol-induced allergy signals, revealing that reactions may be much higher than those in collected spontaneous reports. Iodixanol risk exposure was closer to actual pharmaceutical care findings. PSSA application with ≤7-day intervals appears better suited for monitoring late allergic reaction signals with these drugs.

Abstract 5024: Analytical performance of a genome-wide methylome enrichment platform to detect minimal residual disease from plasma-derived cell-free DNA

Abstract Introduction: Plasma-derived cell-free DNA (cfDNA) can be used to identify cancer signals, including minimal residual disease (MRD), in patients who have undergone curative cancer treatments. The cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) methodology is combined with custom algorithms that leverage differentially methylated regions (DMRs) found in cfDNA to distinguish between cancer and non-cancer signals. This novel non-degradative, tissue-agnostic approach was developed to bypass the limitations of bisulfite-sequencing and tissue-informed methods used in other liquid biopsy technologies. Here we present preliminary analytical performance metrics of an algorithm in development for detecting MRD. Methods: In the genome-wide methylome enrichment platform, plasma-derived cfDNA is subjected to standard library preparation, combined with DNA filler, denatured, and immunoprecipitated using an anti-5-mC antibody. Captured DNA is amplified and sequenced. For this study, a candidate algorithm comprised of differentially methylated regions (DMRs) and 12 cancer types was used to quantify cancer-specific methylation. Control samples from 12 non-cancer donors were used to establish a selected 95% true negative rate threshold. This threshold was used to determine assay sensitivity, which was evaluated using contrived cancer samples intended to mimic low-level circulating tumor DNA (ctDNA) representative of MRD. Samples were contrived by titrating fragmented DNA from three immortalized tumor-derived cell-lines into plasma-derived cfDNA in a titration series targeting <1% ctDNA levels. Cell-lines were of non-small cell lung cancer and head and neck squamous cell carcinoma origin; titration series were replicated for a total of 65 tests. Results: All non-cancer and contrived cancer samples met in-process quality control metrics. This included ≥98.5% methylation specificity and ≥80 million unique molecules. Limit of detection calculations at 95% sensitivity (LoD95) were <0.1%. Conclusions: Tissue-agnostic approaches for detecting cancer signals from plasma have significant benefit, especially in settings where tissue is not accessible for evaluation. However, these tests must have highly sensitive methods of cancer signal detection for clinical applications. Our preliminary analytical data demonstrates the use of a blood-based, tissue- agnostic genome-wide methylome enrichment platform utilizing non-degradative methodology combined with specific algorithms and DMRs for MRD quantification and prognostic prediction, using treatment-naïve plasma samples. Future studies in post-treatment and longitudinal samples are ongoing to evaluate the utility of this genome-wide methylome enrichment platform for cancer management. Citation Format: Shu Yi Shen, Iulia Cirlan, Felicia Vincelli, Ben Brown, Jun Min, Justin Burgener, Junjun Zhang, Yulia Newton, Margaret Gruca, Abel Licon, Jing Zhang, Anne-Renee Hartman, Alan Williams, Hestia Mellert, Daniel D. De Carvalho. Analytical performance of a genome-wide methylome enrichment platform to detect minimal residual disease from plasma-derived cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5024.

Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR

In complex electromagnetic environments, satellite telemetry, tracking, and command (TT&C) signals often become submerged in background noise. Traditional TT&C signal detection algorithms suffer a significant performance degradation or can even be difficult to execute when phase information is absent. Currently, deep-learning-based detection algorithms often rely on expert-experience-driven post-processing steps, failing to achieve end-to-end signal detection. To address the aforementioned limitations of existing algorithms, we propose an intelligent satellite TT&C signal detection method based on triplet attention and Transformer (TATR). TATR introduces the residual triplet attention (ResTA) backbone network, which effectively combines spectral feature channels, frequency, and amplitude dimensions almost without introducing additional parameters. In signal detection, TATR employs a multi-head self-attention mechanism to effectively address the long-range dependency issue in spectral information. Moreover, the prediction-box-matching module based on the Hungarian algorithm eliminates the need for non-maximum suppression (NMS) post-processing steps, transforming the signal detection problem into a set prediction problem and enabling parallel output of the detection results. TATR combines the global attention capability of ResTA with the local self-attention capability of Transformer. Experimental results demonstrate that utilizing only the signal spectrum amplitude information, TATR achieves accurate detection of weak TT&C signals with signal-to-noise ratios (SNRs) of −15 dB and above (mAP@0.5 > 90%), with parameter estimation errors below 3%, which outperforms typical target detection methods.

Blind passive signal detection via dictionary learning in unknown multipath time-spreading distortion underwater channels

Blind passive signal detection is a challenging endeavor since typically there is no prior knowledge of the transmitted signal. This becomes further complicated in time-spreading distortion (TSD) underwater channels [R. Rashid, E. Zhang, A. Abdi, and Z. H. Michalopoulou, “Theoretical and experimental multi-sensor signal detection in time spreading distortion underwater channels,” Proc. Oceans (2022)], where due to the presence of multiple propagation paths, the unknown signal is convolved with an unknown channel impulse response. In this paper, we introduce and develop a blind passive signal detection method, using dictionary learning [M. Sadeghi, M. Babaie-Zadeh, and C. Jutten, “Dictionary learning for sparse representation: A novel approach,” IEEE Signal Process. Lett. (2013)]. In our blind passive signal detection method, we use the received data to estimate the unknown signal, and also to separate it from the unknown channel impulse response. We have conducted simulations and underwater experiments, to generate receiver operating characteristic curves, to study the performance of the proposed method. The high detection probabilities of the blind method, compared to the replica correlation integration method—that needs to know the transmitted signal for matched filtering—demonstrate the usefulness of the method for passive detection of unknown signals in unknown TSD channels.

Quantifying model uncertainty for semantic segmentation of Fluorine-19 MRI using stochastic gradient MCMC

Fluorine-19 (19F) MRI is an emerging theranostic tool for studying diseases and treatments simultaneously, particularly in challenging neuroinflammatory conditions. However, the low signal-to-noise ratio (SNR) of 19F MRI necessitates computational methods to reliably detect 19F signal regions and segment these from the background. In this study, we demonstrate that Bayesian fully convolutional neural networks provide a means to increase sensitivity in 19F MRI and simultaneously provide estimates of data uncertainty. While our model effectively denoises the data, uncertain areas remain, particularly in boundary regions of the foreground. The uncertainty estimates are beneficial in preventing overconfident downstream analysis on noisy data and providing crucial information for rectifying prediction errors. Our results demonstrate that our model significantly outperforms other commonly used methods for 19F MRI signal detection in terms of sensitivity, while also providing valuable uncertainty estimates.

Sensitive Methods to Detect Single-Stranded Nucleic Acids of Food Pathogens Based on Cell-Free Protein Synthesis and Retroreflection Signal Detection.

Cell-free protein synthesis (CFPS) has recently gained considerable attention as a new platform for developing methods to detect various molecules, ranging from small chemicals to biological macromolecules. Retroreflection has been used as an alternative signal to develop analytical methods because it can be detected by using a simple instrument comprising a white light source and a camera. Here, we report a novel reporter protein that couples the capability of CFPS and the simplicity of retroreflection signal detection. The design of the reporter was based on two pairs of protein-peptide interactions, SpyCatcher003-SpyTag003 and MDM2-PMI(N8A). MDM2-MDM2-SpyCatcher003 was decided as the reporter protein, and the two peptides, SpyTag003 and PMI(N8A), were immobilized on the surfaces of retroreflective Janus particles and microfluidic chips, respectively. The developed retroreflection signal detection system was combined with a previously reported CFPS reaction that can transduce the presence of a single-stranded nucleic acid into protein synthesis. The resulting methods were applied to detect 16S rRNAs of several foodborne pathogens. Concentration-dependent relationships were observed over a range of 10° fM to 102 pM, with the limits of detection being single-digit femtomolar concentrations. Considering the designability of the CFPS system for other targets, the retroreflection signal detection method will enable the development of novel methods to detect various molecules.