True Signal Research Articles

CNN-Transformer for Microseismic Signal Classification

The microseismic signals of coal and rock fractures collected by underground sensors contain masses of blasting vibration signals generated by coal mine blasting, and the waveforms of the two signals are highly similar. In order to identify the true microseismic signals with a microseismic monitoring system quickly and accurately, this paper proposes a lightweight network model that combines a convolutional neural network (CNN) and transformer, named CCViT. Of these, the CNN is used to extract shallow features locally, and the transformer is used to extract deep features globally. Moreover, a modified channel attention module provides important channel information for the model and suppresses useless information. The experimental results on the dataset used in this paper show that the proposed CCViT model has significant advantages for floating point operations (FLOPs), parameter quantity, and accuracy compared to many advanced network models.

MultiNEP: a multi-omics network enhancement framework for prioritizing disease genes and metabolites simultaneously.

Many studies have successfully used network information to prioritize candidate omics profiles associated with diseases. The metabolome, as the link between genotypes and phenotypes, has accumulated growing attention. Using a "multi-omics" network constructed with a gene-gene network, a metabolite-metabolite network, and a gene-metabolite network to simultaneously prioritize candidate disease-associated metabolites and gene expressions could further utilize gene-metabolite interactions that are not used when prioritizing them separately. However, the number of metabolites is usually 100 times fewer than that of genes. Without accounting for this imbalance issue, we cannot effectively use gene-metabolite interactions when simultaneously prioritizing disease-associated metabolites and genes. Here, we developed a Multi-omics Network Enhancement Prioritization (MultiNEP) framework with a weighting scheme to reweight contributions of different sub-networks in a multi-omics network to effectively prioritize candidate disease-associated metabolites and genes simultaneously. In simulation studies, MultiNEP outperforms competing methods that do not address network imbalances and identifies more true signal genes and metabolites simultaneously when we down-weight relative contributions of the gene-gene network and up-weight that of the metabolite-metabolite network to the gene-metabolite network. Applications to two human cancer cohorts show that MultiNEP prioritizes more cancer-related genes by effectively using both within- and between-omics interactions after handling network imbalance. The developed MultiNEP framework is implemented in an R package and available at: https://github.com/Karenxzr/MultiNep.

New framework to study unmodeled physics from gravitational wave data

A confident discovery of physics beyond what has been consistently modeled from gravitational wave (GW) data requires a technique that can distinguish between noise artifacts and unmodeled signatures while also shedding light on the underlying physics. We propose a new data analysis method, \texttt{SCoRe} (Structured Correlated Residual), to search for unmodeled physics in the GW data which can cover both of these aspects. The method searches for structure in the cross-correlation power spectrum of the residual strain between pairs of GW detectors. It does so by projecting this power spectrum onto a frequency-dependent template. The template may be model-independent or model-dependent and is constructed based on the properties of the GW source parameters. The projection of the residual strain enables the distinction between noise artifacts and any true signal while capturing possible dependence on the GW source parameters. Our method is constructed in a Bayesian framework and we have shown its application on a model-independent toy example and for a model motivated by an effective field theory of gravity. The method developed here will be useful to search for a large variety of new physics and yet-to-be-modeled known physics in the GW data accessible from the current network of LIGO-Virgo-KAGRA detectors and from future earth- and space-based GW detectors such as A+, LISA, Cosmic Explorer, and Einstein Telescope.

The Edge Effect in High-Throughput Proteomics: A Cautionary Tale.

In order for mass spectrometry to continue to grow as a platform for high-throughput clinical and translational research, careful consideration must be given to quality control by ensuring that the assay performs reproducibly and accurately and precisely. In particular, the throughput required for large cohort clinical validation in biomarker discovery and diagnostic screening has driven the growth of multiplexed targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) assays paired with sample preparation and analysis in multiwell plates. However, large scale MS-based proteomics studies are often plagued by batch effects: sources of technical variation in the data, which can arise from a diverse array of sources such as sample preparation batches, different reagent lots, or indeed MS signal drift. These batch effects can confound the detection of true signal differences, resulting in incorrect conclusions being drawn about significant biological effects or lack thereof. Here, we present an intraplate batch effect termed the edge effect arising from temperature gradients in multiwell plates, commonly reported in preclinical cell culture studies but not yet reported in a clinical proteomics setting. We present methods herein to ameliorate the phenomenon including proper assessment of heating techniques for multiwell plates and incorporation of surrogate standards, which can normalize for intraplate variation.

Simultaneous False-Decision Error Rates in Master Protocols with Shared Control: False Discovery Rate Perspective

Master protocol is a type of trial designs where multiple therapies and/or multiple disease populations can be investigated in the same trial. A shared control can be used for multiple therapies to gain operational efficiency and gain attraction to patients. To balance between controlling for false positive rate and having adequate power for detecting true signals, the impact of False Discovery Rate (FDR) is evaluated when multiple investigational drugs are studied in the master protocol. With the shared control group, the “random high” or “random low” in the control group can potentially impact all hypotheses testing that compare each of the test regimens and the control group in terms of probability of having at least one positive hypothesis outcome, or multiple positive outcomes. When regulatory agencies make the decision of approving or declining one or more regimens based on the master protocol design, this introduces a different type of error: simultaneous false-decision error. In this manuscript, we examine in detail the derivations and properties of the simultaneous false-decision error in the master protocol with shared control under the framework of FDR. The simultaneous false-decision error consists of two parts: simultaneous false-discovery rate (SFDR) and simultaneous false non-discovery rate (SFNR). Based on our analytical evaluation and simulations, the magnitude of SFDR and SFNR inflation is small. Therefore, the multiple error rate controls are generally adequate, further adjustment to a pre-specified level on SFDR or SFNR or reduce the alpha allocated to each individual treatment comparison to the shared control is deemed unnecessary.

Deep learning for denoising High-Rate Global Navigation Satellite System data

High-rate global navigation satellite system (HR-GNSS) data records ground displacements and can be used to identify earthquakes and slow slip events. One limitation of such data is the high amplitude, cm-level noise which make it difficult to identify processes that produce surface displacements smaller than these values. Deep learning has proven adept at performing many useful tasks in seismology and geophysics. Here we explore using deep learning to denoise HR-GNSS data. We develop three different convolutional neural networks with similar architectures but different targets. Training data are synthetic HR-GNSS records and actual noise recordings that are superimposed to generate noisy signals. We train each of the three models to output masks that can be used to reconstruct the true signal. We use a set of performance metrics that quantify the models’ ability to denoise the testing data and find that denoising significantly improves the signal-to-noise ratio and the ability to identify first arrivals. Finally, we test the models on HR-GNSS records from the Ridgecrest earthquakes recorded at stations that have nearly colocated strong-motion sites used ground-truth the denoising results. We find that the models greatly improve the signal-to-noise ratios in these records and make the P-wave onset clearly identifiable.

Adaptive spectroscopic visible-light optical coherence tomography for clinical retinal oximetry

BackgroundRetinal oxygen saturation (sO2) provides essential information about the eye’s response to pathological changes that can result in vision loss. Visible-light optical coherence tomography (vis-OCT) is a noninvasive tool that has the potential to measure retinal sO2 in a clinical setting. However, its reliability is currently limited by unwanted signals referred to as spectral contaminants (SCs), and a comprehensive strategy to isolate true oxygen-dependent signals from SCs in vis-OCT is lacking.MethodsWe develop an adaptive spectroscopic vis-OCT (ADS-vis-OCT) technique that can adaptively remove SCs and accurately measure sO2 under the unique conditions of each vessel. We also validate the accuracy of ADS-vis-OCT using ex vivo blood phantoms and assess its repeatability in the retina of healthy volunteers.ResultsIn ex vivo blood phantoms, ADS-vis-OCT agrees with a blood gas machine with only a 1% bias in samples with sO2 ranging from 0% to 100%. In the human retina, the root mean squared error between sO2 values in major arteries measured by ADS-vis-OCT and a pulse oximeter is 2.1% across 18 research participants. Additionally, the standard deviations of repeated ADS-vis-OCT measurements of sO2 values in smaller arteries and veins are 2.5% and 2.3%, respectively. Non-adaptive methods do not achieve comparable repeatabilities from healthy volunteers.ConclusionsADS-vis-OCT effectively removes SCs from human images, yielding accurate and repeatable sO2 measurements in retinal arteries and veins with varying diameters. This work could have important implications for the clinical use of vis-OCT to manage eye diseases.

Pulse overlap artifacts and double quantum coherence spectroscopy.

The double quantum coherence (DQC) signal in nonlinear spectroscopy gives information about the many-body correlation effects not easily available by other methods. The signal is short-lived, consequently, a significant part of it is generated during the pulse overlap. Since the signal is at two times the laser frequency, one may intuitively expect that the pulse overlap-related artifacts are filtered out by the Fourier transform. Here, we show that this is not the case. We perform explicit calculations of phase-modulated two-pulse experiments of a two-level system where the DQC is impossible. Still, we obtain a significant signal at the modulation frequency, which corresponds to the DQC, while the Fourier transform over the pulse delay shows a double frequency. We repeat the calculations with a three-level system where the true DQC signal occurs. We conclude that with realistic dephasing times, the pulse-overlap artifact can be significantly stronger than the DQC signal. Our results call for great care when analyzing such experiments. As a rule of thumb, we recommend that only delays larger than 1.5 times the pulse length should be used.

Novel targeting assay uncovers targeting information within peroxisomal ABC transporter Pxa1

The mechanism behind peroxisomal membrane protein targeting is still poorly understood, with only two yeast proteins believed to be involved and no consensus targeting sequence. Pex19 is thought to bind peroxisomal membrane proteins in the cytosol, and is subsequently recruited by Pex3 at the peroxisomal surface, followed by protein insertion via a mechanism that is as-yet-unknown. However, some peroxisomal membrane proteins still correctly sort in the absence of Pex3 or Pex19, suggesting that multiple sorting pathways exist. Here, we studied sorting of yeast peroxisomal ABC transporter Pxa1. Co-localisation analysis of Pxa1-GFP in a collection of 86 peroxisome-related deletion strains revealed that Pxa1 sorting requires Pex3 and Pex19, while none of the other 84 proteins tested were essential. To identify regions with peroxisomal targeting information in Pxa1, we developed a novel in vivo re-targeting assay, using a reporter consisting of the mitochondrial ABC transporter Mdl1 lacking its N-terminal mitochondrial targeting signal. Using this assay, we showed that the N-terminal 95 residues of Pxa1 are sufficient for retargeting this reporter to peroxisomes. Interestingly, truncated Pxa1 lacking residues 1–95 still localised to peroxisomes. This was confirmed via localisation of various Pxa1 truncation and deletion constructs. However, localisation of Pxa1 lacking residues 1–95 depended on the presence of its interaction partner Pxa2, indicating that this truncated protein does not contain a true targeting signal.

Abstract 2089: Vanishing deviance problem in high-dimensional penalized Cox regression

Abstract Penalized regression models, such as LASSO and elastic net, have been well-established for high-dimensional variable sections. For the analysis of censored survival outcomes, the penalized Cox regression model has been widely used. The parameter tuning in the penalized regression is often implemented based on the cross-validation (CV) via the “one standard error rule” (1se rule), resulting in a more parsimonious model. However, in a high-dimensional Cox regression model, a common observation is that the CV plot for tuning parameters against partial-likelihood deviance is often not U-shaped but L-shaped. In this case, the model selection will fail with either the “1se” or the“min” rule. We observed that this phenomenon is quite universal in the setting of high-dimensional prognostic biomarker selection, in which the standard CV steps often result in no or very few features included in the final model. We refer to this phenomenon as the “vanishing deviance” (VD) problem. In this study, we investigated this problem in more depth and demonstrated the issue and potential solution using real data from pan-cancer analysis of prognostic lncRNA biomarkers. To discover the cause behind the VD problem, we conducted an extensive set of simulations by varying sample sizes, feature dimensions, censoring rates, number of true biomarkers, and significance levels of true signals.Our results indicated that the main cause of the VD problem can be related to the limited sample size in the cancer omics dataset and a large number of true (or highly associated) biomarkers or signals underlying. To this end, we propose computational solutions that can alleviate the problem and we implemented them in the R package called CoxHive. We verified the performance of our solution based on both simulation studies and the analyses of the pan-cancer gene expression data. This software tool will greatly facilitate biomarker selection (including genomic and imaging biomarkers) in the high-dimensional setting. Citation Format: Sijie Yao, Tingyi Li, Biwei Cao, Xuefeng Wang. Vanishing deviance problem in high-dimensional penalized Cox regression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2089.

TOPAS-imaging: extensions to the TOPAS simulation toolkit for medical imaging systems

Objective. The TOol for PArticle Simulation (TOPAS) is a Geant4-based Monte Carlo software application that has been used for both research and clinical studies in medical physics. So far, most users of TOPAS have focused on radiotherapy-related studies, such as modeling radiation therapy delivery systems or patient dose calculation. Here, we present the first set of TOPAS extensions to make it easier for TOPAS users to model medical imaging systems. Approach. We used the extension system of TOPAS to implement pre-built, user-configurable geometry components such as detectors (e.g. flat-panel and multi-planar detectors) for various imaging modalities and pre-built, user-configurable scorers for medical imaging systems (e.g. digitizer chain). Main results. We developed a flexible set of extensions that can be adapted to solve research questions for a variety of imaging modalities. We then utilized these extensions to model specific examples of cone-beam CT (CBCT), positron emission tomography (PET), and prompt gamma (PG) systems. The first of these new geometry components, the FlatImager, was used to model example CBCT and PG systems. Detected signals were accumulated in each detector pixel to obtain the intensity of x-rays penetrating objects or prompt gammas from proton-nuclear interaction. The second of these new geometry components, the RingImager, was used to model an example PET system. Positron–electron annihilation signals were recorded in crystals of the RingImager and coincidences were detected. The simulated data were processed using corresponding post-processing algorithms for each modality and obtained results in good agreement with the expected true signals or experimental measurement. Significance. The newly developed extension is a first step to making it easier for TOPAS users to build and simulate medical imaging systems. Together with existing TOPAS tools, this extension can help integrate medical imaging systems with radiotherapy simulations for image-guided radiotherapy.

Intelligent model of radar object images for surveillance radars

The results of developing an intelligent model of radar object images for surveillance radars are presented. The relevance of this work deals with the development of algorithm for automatic processing images of radar objects that provide effective detection of weak true signals due to the accumulation of signal and logical information in the analyzed cell and in its surroundings under interferences. The improvement of air safety tools and the automation of air traffic management processes require effective procedures to process signal information. The issues of more complete use and qualitative improvement of the information-processing capabilities of control systems are also topical, especially in difficult conditions of interfering signals. The basis of this study is the idea of using an intellectual model of radar object images for automatic decision-making on detection and recognition of radar objects, built on the space of semantic features. The main result is optical object recognition, similar to how an expert can easily recognize aerial objects and their types when viewing radar object images. Based on semantic features intelligent model of radar object images has been developed, which makes it possible to effectively detect and classify aerial objects. It is worth noting that the characteristic description of intelligent model of radar object images for point, extended, moving and stationary radar objects is the mathematical description of procedures and relationships at perception and analysis of signals in the form of distinguishing features or properties. As a result, various virtual images of radar object are generated in the form of spatial-semantic and spectral-semantic models. The main features and structural elements of the model are given. It is shown that the advantages of this model are related to the possibility of characteristic description of the radar object images using the algebra of finite predicates.

Identifying regions of interest in mammogram images.

Screening mammography is the primary preventive strategy for early detection of breast cancer and an essential input to breast cancer risk prediction and application of prevention/risk management guidelines. Identifying regions of interest within mammogram images that are associated with 5- or 10-year breast cancer risk is therefore clinically meaningful. The problem is complicated by the irregular boundary issue posed by the semi-circular domain of the breast area within mammograms. Accommodating the irregular domain is especially crucial when identifying regions of interest, as the true signal comes only from the semi-circular domain of the breast region, and noise elsewhere. We address these challenges by introducing a proportional hazards model with imaging predictors characterized by bivariate splines over triangulation. The model sparsity is enforced with the group lasso penalty function. We apply the proposed method to the motivating Joanne Knight Breast Health Cohort to illustrate important risk patterns and show that the proposed method is able to achieve higher discriminatory performance.

The reliable sampling interval for monitoring interannual variability of the Kuroshio transport at 18°N

The sampling interval for reliably monitoring interannual variability of the Kuroshio transport was studied using satellite altimeter data and outputs from HYbrid Coordinate Ocean Model (HYCOM). Interannual variations of the Kuroshio velocity and transport crossing 18°N were derived with different sampling interval, and their deviations from the ‘true’ interannual signals derived from original time series were estimated. The results indicate that sampling with an interval shorter than 20 days is required to obtain the reliable interannual signals, i.e., the Root Mean Square of the deviation is <50% of the interannual signal. However, the longer sampling intervals will lead to serious aliasing and degradation of the true interannual signals, which is mainly attributed to the energetic intraseasonal oscillations in this region. Power spectrum analysis reveal that there are significant 80-day intraseasonal variations as well as semiannual fluctuations in the Kuroshio transport at 18°N, which brings substantial sampling deviations relative to the true interannual variation. An ideal experiment is constructed to confirm that the reliable sampling interval of around 20 days, which can capture interannual variations and resolve the intraseasonal oscillation of the Kuroshio reliably at 18°N. This study provides an important reference for the future monitoring of the interannual variation of the Kuroshio.

Inhomogeneous regularization with limited and indirect data

For an ill-posed inverse problem, particularly with incomplete and limited measurement data, regularization is an essential tool for stabilizing the inverse problem. Among various forms of regularization, the ℓp penalty term provides a suite of regularization of various characteristics depending on the value of p. When there are no explicit features to determine p, a spatially varying inhomogeneous p can be incorporated to apply different regularization characteristics that change over the domain. This study proposes a strategy to design the exponent p when the first and second derivatives of the true signal are not available, such as in the case of indirect and limited measurement data. The proposed method extracts statistical and patch-wise information using multiple reconstructions from a single measurement, which assists in classifying each patch to predefined features with corresponding p values. We validate the robustness and effectiveness of the proposed approach through a suite of numerical tests in 1D and 2D, including a sea ice image recovery from partial Fourier measurement data. Numerical tests show that the exponent distribution is insensitive to the choice of multiple reconstructions.

Intraoperative neuromonitoring for pediatric Chiari decompression: when is it useful?

Surgical treatment for symptomatic Chiari I malformation involves surgical decompression of the craniovertebral junction. Given the proximity of critical brainstem structures, intraoperative neuromonitoring (IONM) is employed for safe decompression in some institutions. However, IONM adds time and cost to the operation, and the benefit to the patient has not been defined. Given the diversity in surgical practices, there is no evidence-based standard of care regarding when to use IONM and which modalities are most helpful. The purpose of this study was to review a single-surgeon experience with IONM in order to determine the sensitivity, specificity, and predictive values of various IONM modalities routinely used in pediatric Chiari I decompression; to examine the associations between patient, clinical, and radiographic characteristics and IONM alerts; and to obtain data regarding the usefulness of these modalities during the surgical process to improve patient outcomes. A retrospective review was performed for 300 consecutive pediatric patients who underwent suboccipital craniectomy and C1 laminectomy for Chiari decompression performed by a single surgeon over a 15-year period. Clinical, radiographic, and IONM data were collected. Radiographic measurements of the skull base morphological abnormalities, including clival angle, Chamberlain's line, and Grabb-Oakes line, were compared between patients with and without true IONM signal changes. A total of 291 cases were included, with an age range of 6 months to 19 years. Among 291 cases, somatosensory evoked potentials (SSEPs) were monitored in 291, motor evoked potentials (MEPs) in 209, cranial nerve spontaneous electromyography (sEMG) in 290, and brainstem auditory evoked potentials (BAEPs) in 110. Sensitivity, specificity, positive predictive value, and negative predictive value, respectively, were as follows: 1.00, 1.00, 1.00, and 1.00 for SSEPs; 1.00, 0.99, 0.67, and 1.00 for MEPs; 0.00, 0.88, 0.00, and 1.00 for sEMG; and not appliable, 1.00, not applicable, and 1.00 for BAEPs. Six patients had true IONM signal changes. These patients had radiographic evidence of more severe concomitant craniocervical instability and basilar invagination, with steeper clival angles (124° vs 146°, p = 0.02) and larger Grabb-Oakes lines (10.1 mm vs 6.7 mm, p = 0.02), when compared with the patients without any true IONM changes. Intraoperative neuromonitoring may be best utilized for patients who show radiographic features of abnormal skull base morphology, defined as a clival angle < 135° or Grabb-Oakes line > 9 mm. When IONM is employed, SSEP and MEP monitoring are the most useful modalities.

Selective footprints and genes relevant to cold adaptation and other phenotypic traits are unscrambled in the genomes of divergently selected chicken breeds

BackgroundThe genomes of worldwide poultry breeds divergently selected for performance and other phenotypic traits may also be affected by, and formed due to, past and current admixture events. Adaptation to diverse environments, including acclimation to harsh climatic conditions, has also left selection footprints in breed genomes.ResultsUsing the Chicken 50K_CobbCons SNP chip, we genotyped four divergently selected breeds: two aboriginal, cold tolerant Ushanka and Orloff Mille Fleur, one egg-type Russian White subjected to artificial selection for cold tolerance, and one meat-type White Cornish. Signals of selective sweeps were determined in the studied breeds using three methods: (1) assessment of runs of homozygosity islands, (2) FST based population differential analysis, and (3) haplotype differentiation analysis. Genomic regions of true selection signatures were identified by two or more methods or in two or more breeds. In these regions, we detected 540 prioritized candidate genes supplemented them with those that occurred in one breed using one statistic and were suggested in other studies. Amongst them, SOX5, ME3, ZNF536, WWP1, RIPK2, OSGIN2, DECR1, TPO, PPARGC1A, BDNF, MSTN, and beta-keratin genes can be especially mentioned as candidates for cold adaptation. Epigenetic factors may be involved in regulating some of these important genes (e.g., TPO and BDNF).ConclusionBased on a genome-wide scan, our findings can help dissect the genetic architecture underlying various phenotypic traits in chicken breeds. These include genes representing the sine qua non for adaptation to harsh environments. Cold tolerance in acclimated chicken breeds may be developed following one of few specific gene expression mechanisms or more than one overlapping response known in cold-exposed individuals, and this warrants further investigation.

Random code shifting based ultra-wideband photonic compressive receiver with image-frequency distinction.

We propose an ultra-wideband photonic compressive receiver based on random codes shifting with image-frequency distinction. By shifting the center frequencies of two random codes in large frequency range, the receiving bandwidth is flexibly expanded. Simultaneously, the center frequencies of two random codes are slightly different. This difference is used to distinguish the "fixed" true RF signal from the differently located image-frequency signal. Based on this idea, our system solves the problem of limited receiving bandwidth of existing photonic compressive receivers. In the experiments, with two channels of only 780-MHz outputs, the sensing capability in the range of 11-41 GHz has been demonstrated. A multi-tone spectrum and a sparse radar-communication spectrum, composed of a linear frequency modulated (LFM) signal, a quadrature phase-shift keying (QPSK) signal and a single-tone signal, are both recovered.

A new paradigm of reliable sensing with field-deployed electrochemical sensors integrating data redundancy and source credibility

For a continuous healthcare or environmental monitoring system, it is essential to reliably sense the analyte concentration reported by electrochemical sensors. However, environmental perturbation, sensor drift, and power-constraint make reliable sensing with wearable and implantable sensors difficult. While most studies focus on improving sensor stability and precision by increasing the system’s complexity and cost, we aim to address this challenge using low-cost sensors. To obtain the desired accuracy from low-cost sensors, we borrow two fundamental concepts from communication theory and computer science. First, inspired by reliable data transmission over a noisy communication channel by incorporating redundancy, we propose to measure the same quantity (i.e., analyte concentration) with multiple sensors. Second, we estimate the true signal by aggregating the output of the sensors based on their credibility, a technique originally developed for “truth discovery” in social sensing applications. We use the Maximum Likelihood Estimation to estimate the true signal and the credibility index of the sensors over time. Using the estimated signal, we develop an on-the-fly drift-correction method to make unreliable sensors reliable by correcting any systematic drifts during operation. Our approach can determine solution pH within 0.09 pH for more than three months by detecting and correcting the gradual drift of pH sensors as a function of gamma-ray irradiation. In the field study, we validate our method by measuring nitrate levels in an agricultural field onsite over 22 days within 0.06 mM of a high-precision laboratory-based sensor. We theoretically demonstrate and numerically validate that our approach can estimate the true signal even when the majority (~ 80%) of the sensors are unreliable. Moreover, by restricting wireless transmission to high-credible sensors, we achieve near-perfect information transfer at a fraction of the energy cost. The high-precision sensing with low-cost sensors at reduced transmission cost will pave the way for pervasive in-field sensing with electrochemical sensors. The approach is general and can improve the accuracy of any field-deployed sensors undergoing drift and degradation during operation.

LiveG4ID-Seq for Profiling the Dynamic Landscape of Chromatin G-Quadruplexes During Cell Cycle in Living Cells.

G-quadruplex (G4) structures exist in the single-stranded DNA of chromatin and regulate genome function. However, the native chromatin G4 landscape in living cells has yet to be fully characterized. Herein, a genetic-encoded live-cell G4 identifier probe (LiveG4ID) is constructed and its cellular localization, biocompatibility, and G4-binding specificity is evaluated. By coupling LiveG4ID with cleavage under targets and tagmentation (CUT&Tag), LiveG4ID-seq, a method for mapping native chromatin G4 landscape in living cells with high accuracy is established. Compared to the conventional G4 CUT&Tag method, LiveG4ID-seq can identify more chromatin G4 signals and have a higher ratio of true positive signals. Using LiveG4ID-seq, the dynamic landscape of chromatin G4 structures during the cell cycle is profiled. It is discovered that chromatin G4 structures are prevalent in the promoter regions of cell cycle-specific genes, even in the early M phase when the chromatin is condensed. These data demonstrate the capacity of LiveG4ID-seq to profile a more accurate G4 landscape in living cells and promote future studies on chromatin G4 structures.