False Signals Research Articles

A single-step, digital immunoassay based on serial imaging and image processing

Single-step immunoassays have attracted much attention compared with conventional multi-step sandwich immunoassays, owing to their user-friendliness, minimization of potential errors (e.g., false signals) arising from multistep procedures, and reduced diagnosis time. However, developing a single-step immunoassay with high sensitivity and accuracy has remained challenging. In this study, we developed a single-step, digital immunoassay by applying an imaging technique to a typical sandwich immunoassay, leading to high sensitivity and accuracy. The imaging technology—composed of serial imaging of a sensing surface, stacking of the obtained images, and particle counting using an algorithm—enabled the differentiation of bound and unbound conjugates (detection antibody-conjugated polystyrene beads) without washing. Stationary bound conjugates became more distinct after stacking over 30 images, while randomly drifting unbound conjugates disappeared, enabling the counting of only individual bound labels. The platform efficiently detected SARS-CoV-2 nucleocapsid protein in the concentration range of 10 pg mL–1 to 1 ng mL–1 (limit of detection, 0.87 pg mL–1), which was 103 times more sensitive than that of a conventional lateral flow immunoassay. This immunoassay could be a promising method for highly sensitive and accurate point-of-care biosensors.

Monitoring the Intracellular Fate of Molecular Beacons: The Challenge of False Positive Signals

Molecular beacons (MBs) have been used on surfaces for detecting oligonucleotides. Attempts to use them intracellularly for monitoring mRNA content have been made, however, without any clear conclusion regarding the reliability of the method, mainly due to false positive signals. To reach an understanding of the intracellular fate of MBs, a critical question remains: how long after MB delivery and where in the cell does a false positive signal appear? To answer that question, the MB delivery method should allow for a time‐stamped synchronized delivery of MBs to multiple cells, resulting in MBs being distributed in the cytosol immediately after delivery. Herein, nanostraws are used to inject MBs targeting insulin (Ins1) mRNA directly in the cytosol of clonal beta‐cells, and the evolution of the MB fluorescence in time and space is monitored. The results show an MB translocation to the nucleus, where MBs are degraded or where they open nonspecifically, before the fluorophore alone is expelled back from the nucleus to the cytosol. The signal translocation to the nucleus and back to the cytosol is faster when scrambled MBs are used. The results shed light on the intracellular fate of MBs and highlight the short time scales before false positive signals become predominant.

Unique fluorescent probe for the recognition of late apoptosis via translocation from plasma membrane to nucleus

Cell apoptosis is a crucial physiological process playing central roles in key biological and pathological activities. However, the current fluorescent probes for the detection of late apoptosis were “off–on” probes, which were facilely interfered by false positive signals caused by inhomogeneous staining and other factors. Herein, a unique fluorescent probe (NPn) discriminating late apoptosis from early apoptosis and heathy status with two different sets of fluorescent signals have been prepared, to overcome the possible false positive signals. NPn was designed impermeable to biomembranes and simultaneously with high affinity to DNA/RNA, which localized on the plasma membranes of living and early apoptotic cells, while relocated to the nucleus in late apoptotic cells. The hydrophilic amine unit and small ion radius were responsive for its membrane impermeability, which was confirmed with two control molecules without amine group. Using the probe, we have successfully evaluated the cell apoptosis induced by ultraviolet irradiation, rotenone, colchicine, and paclitaxel, demonstrating its potential application in biological researches.

Fine-tuning of highly bright benzo[c,d]indole-oxazolopyridine cyanine dye for nucleolar RNA imaging in living cells

Here we report on fine-tuning of highly bright fluorogenic cyanine dye, benzo[c,d]indole-oxazolo[5,4-c]pyridine (BIOP: λem = 570 nm, Φfree = 0.00038, Φbound = 0.52), recently developed by our group for nucleolar RNA imaging in living cells. We tuned an emission maximum to the longer wavelength by replacing oxazolopyridine unit with its isomer. The resulting probe with oxazolo[4,5-b]pyridine, named BIOP [4,5-b], exhibited a significant off-on signaling ability for RNA (λem = 580 nm, Φfree = 0.002, Φbound = 0.46) that almost compared with BIOP. BIOP [4,5-b] was applicable to live-cell imaging, where wash-free protocol was available. Importantly, the slight change in spectral features would be expected to minimize the false signal from BIOP [4,5-b] in co-staining experiments with typical green-emissive dyes. In addition, thanks to the unique spectral shape that is typical of cyanine dyes, we demonstrate that yellow-emissive BIOP [4,5-b] also did work as a pseudo red-emissive dye (λem > 600 nm) for live-cell RNA imaging, for which we just switch filter set to typical one for real red-emissive dyes (Ex 560/40 nm; Em 645/75 nm).

Nonradical Surface Chemistry Mechanisms for Catalytic Nanoparticles.

All radical-detecting methods using trapping agents, which are originally developed for homogeneous reaction systems, may not be applicable to systems with solid surfaces. This is because false radical signals can be generated in the presence of solid surfaces. An extra selectivity study following the trapping agent experiment may help in distinguishing between the true and false radical signals. Surface chemistry mechanisms are superior to free-radical mechanisms in not only correctly understanding the reaction selectivity previously reported for catalytic nanoparticles but also developing theoretical models for the computational design of solid catalysts in the future.

Automatic Dependent Surveillance-Broadcast Deceptive Jamming Detection Method Based on Track Data

The characteristics of automatic dependent surveillance-broadcast (ADS-B) technology, such as unencrypted channels and open data protocols, make it extremely vulnerable to various intentional or unintentional spoofing and jamming, which seriously threatens air traffic safety. Traditional approaches do not update their high-bit data for a long time, which causes the situations of the same encoding results to have different aircraft positions and it is difficult to determine the real position of the aircraft and effectively detect ADS-B jamming. Therefore, it is urgent to propose more effective detection methods for ADS-B spoofing and jamming. First, the methods and principles of frequency shift calculation and automatic monitoring algorithm are introduced. Then, signal consistency detection and error robustness are analyzed, and a track-data-based detection model for abnormal ADS-B data are proposed. The track-data-based detection methods can use multi-point sampling judgment algorithm to realize the reception and processing of data bits. This method makes full use of the information of multiple sampling values of each data bit and the reference power value obtained in the preamble detection, and determines the data value and confidence value through the relationship between the two. The detection methods for ADS-B spoofing and jamming distinguishes the true and false signals and the authenticity of target positions by comparing the arrival time difference of each signal at the ground station. Finally, the research results show that, with the increase of signal to noise ratio, no matter the estimation based on virtual array translation or the estimation based on circular stationary signal, their estimation accuracies are improved with gradually increasing trends.

Early warning system for currency crises using long short‐term memory and gated recurrent unit neural networks

AbstractCurrency crises, recurrent events in the economic history of developing, emerging, and developed countries, have disastrous economic consequences. This paper proposes an early warning system for currency crises using sophisticated recurrent neural networks, such as long short‐term memory (LSTM) and gated recurrent unit (GRU). These models were initially used in language processing, where they performed well. Such models are increasingly being used in forecasting financial asset prices, including exchange rates, but they have not yet been applied to the prediction of currency crises. As for all recurrent neural networks, they allow for taking into account nonlinear interactions between variables and the influence of past data in a dynamic form. For a set of 68 countries including developed, emerging, and developing economies over the period of 1995–2020, LSTM and GRU outperformed our benchmark models. LSTM and GRU correctly sent continuous signals within a 2‐year warning window to alert for 91% of the crises. For the LSTM, false signals represent only 14% of the emitted signals compared with 23% for logistic regression, making it an efficient early warning system for policymakers.

Improvement of S/N ratio in simultaneous photoacoustic and photoluminescence measurements by utilizing Helmholtz resonance

Accurate estimation of internal quantum efficiency (IQE) is essential for a comprehensive understanding of carrier dynamics in light-emitting materials in optical devices. We proposed simultaneous photoacoustic (PA) and photoluminescence measurements as a method for estimating accurate IQE values. This method detects the heat generated by non-radiative recombination as well as the light generated by radiative recombination, and this reduces the need to use theoretical models and assumptions for IQE estimation, unlike conventional methods that measure only radiative recombination processes. In some cases, however, there is a problem of heat generated outside of the measurement point being mixed into the PA signal. HF measurement reduces the mixing of false signals, but the PA signal intensity itself deceases with frequency, resulting in a poor S/N ratio. In this study, we have significantly improved the S/N ratio of PA measurements at HF by two measures in the microphone method we use for PA measurements: increasing the air pressure in the cell and utilizing Helmholtz resonance.

Synthesis of a measurement procedure for estimating the orientation of a small unmanned aerial vehicle under conditions of changing status of measurement results

The problem of processing measurement information with changing status of measurement results of micromechanical sensors of an intelligent on-board measuring system of a small unmanned aerial vehicle is considered. While vehicle is in the air, the measurement results status changes from confirmed to orienting, for example, due to sensor defects, degradation of the measuring channel, the appearance of false measurement output signals of micromechanical sensors due to vibrations and shocks caused by the movement of air masses. As a result, the probability of stability loss of a small unmanned aerial vehicle increases and it is necessary to raise the accuracy of estimating its orientation in conditions of changing status of measurement results. The measuring procedure of the Kalman structure is considered, the equations of which are determined with accuracy to the parameters of the transition and noise matrices of the state, as well as the perturbation vector, characterizing the measuring process. The parameter values are determined by a mathematical model for converting measuring information based on a dynamic mathematical model, which distinguishes the developed measuring procedure from a measuring procedure with a classical transition matrix. A neural network is used to find unknown parameters. A multilayer perceptron was selected as the basis of a neural network, for which an error back propagation algorithm is used to train. Based on the results of mathematical modeling and measurement experiment, it was found that the accuracy of the synthesized measuring procedure based on a dynamic mathematical model is higher than the accuracy of the measuring procedure of the Kalman structure with a classical transition matrix. The results of the study will be useful in the development of intelligent measurement procedures for on-board measurement systems operating under conditions of changing status of measurement results

Delta4Ms: Improving mutation‐based fault localization by eliminating mutant bias

AbstractFault localization is a complex, costly and time‐consuming task in software debugging. Numerous automated techniques have been developed to expedite this process. Mutation‐based fault localization (MBFL) is one of the most widely studied techniques which uses mutation analysis to generate mutants for revealing potential faults in the program. However, our theoretical analysis exposes an inherent conflict between the fundamental assumption and the essential meaning of existing MBFL suspiciousness. This conflict is caused by mutant bias. Intuitively, the suspiciousness can be corrected by eliminating the mutant bias for more accurately measuring the faulty probability of the corresponding mutant statement. In this paper, we introduce Delta4Ms, a fault localization approach designed to eliminate mutant bias. Delta4Ms integrates the principles of signal theory, modelling the actual suspiciousness and mutant bias as the desired and false signal components, respectively. Based on theoretical derivation, the average suspiciousness of mutants serves as an estimate of mutant bias. Delta4Ms effectively mitigates mutant bias, extracting the desired signal and yielding corrected suspiciousness for fault localization. To precisely estimate mutant bias, higher order mutants (HOMs) are incorporated. We conduct an extensive experimental evaluation of Delta4Ms on 320 real‐fault programs from Codeflaws. The results indicate that our model significantly outperforms existing SBFL and MBFL techniques, showing a considerable improvement in fault localization effectiveness. We further assessed the robustness of Delta4Ms by examining different HOM ratios and HOM generation strategies. Moreover, Delta4Ms achieves a substantial reduction in mutation execution cost and minimal accuracy loss through the implementation of test case reduction. Finally, we perform preliminary experiments on 15 real‐fault programs from the Defects4J benchmark to assess the generalization of the model's fault localization effectiveness.

Increasing Long-Term Memory as an Early Warning Signal for a Critical Transition

Abstract In recent years, various early warning signals of critical transition have been presented, such as autocorrelation at lag 1 [AR(1)], variance, the propagator based on detrended fluctuation analysis (DFA-propagator), and so on. Many studies have shown that the climate system has the characteristics of long-term memory (LTM). Will the LTM characteristics of the climate system change as it approaches possible critical transition points? In view of this, the present paper first studies whether the LTM of several folding (folded bifurcation) models changes consistently as they approach their critical points slowly by the rescaled range (R/S) analysis. The results of numerical experiments show that when the control parameters of the folding model are close to its critical threshold, the Hurst exponent H exhibits an almost monotonic increase (significance level α = 0.05). We compare the performance of R/S with the existing indicators, including AR(1), variance, and DFA-propagator, and find that R/S is a perfectly valid alternative. When there is no extra false noise, AR(1) and variance have good early warning effects. After the addition of extra Gaussian white noise of different intensities, the values of AR(1) and variance change significantly. As a result, the DFA-propagator based on AR(1) calibration also changed significantly. Compared with the other three indicators, the early warning effect of H has stronger ability to resist the interference of external false signals. To further verify the validity of increasing H, paleoclimate reconstruction of Cariaco Basin sediment core grayscale record with long trends filtered out is studied by R/S analysis. The other three early warning signals are calculated in the same way. The data contain a well-known abrupt climate change: the transition between the Younger Dryas (YD) and the Holocene. We find that approximately 300 years before this abrupt climate change occurred, before 11.7 kyr BP, the LTM exponents for Cariaco Basin deglacial grayscale data present an obvious increasing trend at a significant level of α = 0.05. Meanwhile, the variation trend of H and DFA-propagator is basically similar. This shows that increasing H by R/S analysis is an effective early warning signal, which indicates that a dynamic system is approaching its possible critical transition points; H is a completely valid alternative signal for AR(1) and DFA-propagator. The main conclusion of this paper is based on numerical experiments. The precise relationship between H and the stability of the underlying state approaching the transition needs to be further studied. Significance Statement Dynamic systems have critical transition points, and these systems will suddenly change from a stable state to another alternative one beyond these points. Using several simple theoretical models and paleoclimate data, we study whether the characteristics of long-term memory, which are ubiquitous in complex systems in nature and society, change as a system approaches its critical transition point. The results show that the long-term memory of a dynamic system increases significantly with the approach of the critical point, whether in theoretical models or in paleoclimate data.

DNA Gate-Based CRISPR-Cas Exponential Amplification System for Ultrasensitive Small Extracellular Vesicle Detection to Enhance Breast Cancer Diagnosis.

Tumor-derived small extracellular vesicles (tEVs) as potential biomarkers possess abundant surface proteins closely related to parent cells, which are crucial for noninvasive cancer diagnosis. However, tEVs exhibit phenotype heterogeneity and low abundance, posing a significant challenge for multiplex detection with a high sensitivity. Herein, we developed a DNA gate-based exponential amplification CRISPR-Cas (DGEAC) system for accurate and ultrasensitive detection of tEVs, which can greatly improve the accuracy of breast cancer (BC) diagnosis. Based on the coexpression of CD63 and vascular endothelial growth factor (VEGF) on BC-derived tEVs, we developed a dual-aptamer-based AND gate fluorescent probe by proximity hybridization. By integrating the target recognition and trans-cleavage activity of Cas12a, an autocatalysis-driven exponential amplification circuit was developed for ultrasensitive detection of CD63 and VEGF proteins on tEVs, which could avoid false negative signals from single protein or other interfering proteins. We achieved highly sensitive detection of tEVs over a linear range from 1.75 × 103 to 3.5 × 108 particles/mL with a detection limit as low as 1.02 × 103 particles/mL. Furthermore, the DGEAC system can distinguish tEVs from tEVs derived from different BC cell lines, including MDA-MB-231, MCF-7, SKBR3, and MCF-10A. Compared to linear amplification (AUC 90.0%), the DGEAC system effectively differentiates BC in different stages (AUC 98.3%).

Understanding the impacts of dark pools on price discovery

I study how crossing networks, a type of dark pool, affect price discovery and market liquidity in the presence of noisy and heterogeneous trader signals. I identify a buffer function of crossing networks that helps mitigate traders’ losses from false signals. Additionally, I uncover an amplification effect. That is, when signal precision is high, crossing networks enhance price discovery. By contrast, when signal precision is low, crossing networks impair price discovery. These insights reconcile conflicting empirical evidence, yielding novel predictions and regulatory recommendations for equity and emerging markets.

Technical Analysis of Stock Investment Decisions in Telecommunication Companies Listed on the Indonesian Stock Exchange

This research aims to examine stock investment decisions in telecommunications companies listed on the Indonesia Stock Exchange using a technical analysis approach. This research does not use hypotheses, so quantitative descriptive research is used. The purposive sampling method was used to obtain a representative sample in accordance with the sampling criteria, namely telecommunications companies listed on the Indonesia Stock Exchange. The object of the research is data in the form of graphs and trends in monthly stock price movements of three issuers in the fourth quartile of 2023 starting from October-December 2023. The research results show that the analysis of the three stocks above shows that MACD crossovers often give false signals if they are not confirmed by indicator movements momentum like RSI. In the case of TLKM and ISAT, even though the MACD has crossed over, the RSI actually moves in the opposite direction or at least does not provide a strong response, so there is potential for a reversal. Likewise with EXCL where even though a MACD crossover occurred, the RSI did not penetrate a significant level to validate the signal. Overall, MACD crossovers without the support of evidence of changes in momentum from the RSI tend to be weak and inaccurate. It takes confirmation from other indicators for a MACD crossover to truly indicate a long-term trend change instead of just a high-risk temporary move. Thus, it is important to carry out multi-factor validation so that the MACD crossover signal is stronger before being used as a reference for entry or exit positions.

Fluorescent, phosphorescent, magnetic resonance contrast and radioactive tracer labelling of extracellular vesicles.

This review focusses on the significance of fluorescent, phosphorescent labelling and tracking of extracellular vesicles (EVs) for unravelling their biology, pathophysiology, and potential diagnostic and therapeutic uses. Various labeling strategies, such as lipid membrane, surface protein, luminal, nucleic acid, radionuclide, quantum dot labels, and metal complex-based stains, are evaluated for visualizing and characterizing EVs. Direct labelling with fluorescent lipophilic dyes is simple but generally lacks specificity, while surface protein labelling offers selectivity but may affect EV-cell interactions. Luminal and nucleic acid labelling strategies have their own advantages and challenges. Each labelling approach has strengths and weaknesses, which require a suitable probe and technique based on research goals, but new tetranuclear polypyridylruthenium(II) complexes as phosphorescent probes have strong phosphorescence, selective staining, and stability. Future research should prioritize the design of novel fluorescent probes and labelling platforms that can significantly enhance the efficiency, accuracy, and specificity of EV labeling, while preserving their composition and functionality. It is crucial to reduce false positive signals and explore the potential of multimodal imaging techniques to gain comprehensive insights into EVs.

A motion aftereffect from viewing other people's gaze.

Recent work suggests that our brains may generate subtle, false motion signals streaming from other people to the objects of their attention, aiding social cognition. For instance, brief exposure to static images depicting other people gazing at objects made subjects slower at detecting subsequent motion in the direction of gaze, suggesting that looking at someone else's gaze caused a directional motion adaptation. Here we confirm, using a more stringent method, that viewing static images of another person gazing in a particular direction, at an object, produced motion aftereffects in the opposite direction. The aftereffect was manifested as a change in perceptual decision threshold for detecting left vs. right motion. The effect disappeared when the person was looking away from the object. These findings suggest that the attentive gaze of others is encoded as an implied agent-to-object motion that is sufficiently robust to cause genuine motion aftereffects, though subtle enough to remain subthreshold.

Self-evolving persistent luminescence nanoprobes for autofluorescence-free ratiometric imaging and on-demand enhanced chemodynamic therapy of pulmonary metastatic tumors.

Precise imaging-guided therapy of a pulmonary metastasis tumor is of great significance for tumor management and prognosis. Persistent luminescence nanoparticles (PLNPs) are promising probes due to their in situ excitation-free and low-background imaging characteristics. However, most of the PLNP-based probes cannot intelligently distinguish between normal and tumor tissues or balance the needs of targeted accumulation and rapid metabolism, resulting in false positive signals and potential side effects. Besides, the luminescence intensity of single-emissive PLNPs is affected by external factors. Herein, we report a self-evolving double-emissive PLNP-based nanoprobe ZGMC@ZGC-TAT for pulmonary metastatic tumor imaging and therapy. Acid-degradable green-emitting PLNPs (ZGMC) with good afterglow performance and therapeutic potential are synthesized by systematic optimization of dopants. Ultra-small red-emitting PLNPs (ZGC) are then prepared as imaging and reference probes. The two PLNPs are finally covalently coupled and further modified with a cell-penetrating peptide (TAT) to obtain ZGMC@ZGC-TAT. Dual emission ensures a stable luminescence ratio (I700/I537) independent of probe concentration, test voltage and time gate. ZGMC degrades and phosphorescence disappears in a tumor microenvironment (TME), resulting in an increase in I700/I537, thus enabling tumor-specific ratiometric imaging. Cu2+ and Mn2+ released by ZGMC degradation achieve GSH depletion and enhance CDT, effectively inhibiting tumor cell proliferation. Meanwhile, the size of ZGMC@ZGC-TAT decreases sharply, and the resulting ZGC-TAT further causes nuclear pyknosis and quickly clear metabolism. The developed ZGMC@ZGC-TAT turns non-targeted lung aggregation of nanomaterials into a unique advantage, and integrates TME-triggered phosphorescence and size self-evolution, and on-demand therapeutic functions, showing outstanding prospects in precise imaging and efficient treatment of pulmonary metastatic tumors.

Comparative analysis of micronucleus induction and DNA damage biomarkers in TK6 and A375 cells using flow cytometry.

Previously, we introduced an alternative adherent A375 cell line for clastogenicity and aneugenicity testing using a high content imaging platform. To further characterize the performance of A375 cells, we investigated the sensitivity and specificity of A375 and TK6 cells by directly comparing micronucleus (MN) induction, cytotoxicity (relative cell counts, viability, and apoptosis), clastogenicity (γH2AX), and aneuploidy markers (pH 3, MPM-2, and polyploidy) using flow cytometric methods. We evaluated 14 compounds across different mechanisms (non-genotoxic apoptosis inducers, clastogens, and aneugens with either tubulin binding or aurora kinase inhibiting phenotypes) at 4-h and 24-h post treatment. Both aneugens and clastogens tested positive for micronucleus induction in both cell lines. Apoptosis continued to be a confounding factor for flow cytometry-based micronuclei assessment in TK6 cells as evidenced by positive responses by the three cytotoxicants. Conversely, A375 cells were not affected by apoptosis-related false positive signals and did not produce a positive response in the in vitro micronucleus assay. Benchmark dose response (BMD) analysis showed that the induction of micronuclei and biomarkers occurred at similar concentrations in both cell lines for clastogens and aneugens. By showing that A375 cells have similar sensitivity to TK6 cells but a greater specificity, these results provide additional support for A375 cells to be used as an alternative adherent cell line for in vitro genetic toxicology assessment.

Rapid detection of HPV16 utilizing recombinase polymerase amplification with the employment of an extremely low concentration of the probe.

Human papillomavirus 16 (HPV16) infection is the leading cause of cervical cancer. The current mainstream method for detecting HPV16 is quantitative real-time PCR (qPCR). However, due to its time-consuming nature and reliance on expensive qPCR instruments, there is growing interest in more convenient, rapid and private approaches such as recombinase polymerase amplification (RPA). In this study, we innovated an effective method for HPV16 detection by integrating a RPA system with lateral flow strip (LFS) detection. Primers with optimal efficiency and specificity were designed, and false positive signals caused by dimers were eliminated by reducing the probe concentration. The RPA-LFS method demonstrates high sensitivity and specificity, capable of detecting 230 copies per reaction of HPV16 within 25 min without cross-reactivity to other subtypes. It exhibits good tolerance, remaining unaffected by 1.0% miconazole, 0.5% tioconazole and 1.0% hemoglobin. The results of clinical samples detected by this method were consistent with those of qPCR. The method provides a practical reference for HPV16 diagnosis and can be valuable in home and resource-limited settings, contributing to the reduction of cervical cancer incidence.

Применение зондирующих ФКМ-сигналов с нулевой зоной автокорреляции для улучшения качества измерений в РСА

For SARs, a polyphase (p-phase, where p is a prime number) probing signal with a zero autocorrelation zone has been synthesized, which is a train of p phase-code shift keyed (PCSK) pulses coded by complementary sequences of a p-ary D-code with additional phase-code shift-keying of the sub-pulses (discretes) of pulses by an M-sequences. To assess the quality of radar images of point targets and extended surfaces, the levels of the maximum side lobe and one-way integrated correlation noise of the autocorrelation function (ACF) are used, respectively. For this purpose, the work carried out a comparative analysis of the total ACFs of the synthesized ensemble of signals with ensembles of orthogonal chirp and PCSK-signals used in adjacent SAR sounding cycles. The work also considers the problem of superimposition of SAR echo signals from different ranges at a relatively high repetition rate of probing pulses. To assess the level of suppression of recurrent interference from point targets and from extended surfaces, the levels of the maximum side lobe and one-sided integral correlation noise of the cross-correlation function (CCF) are used, respectively. For this purpose, the work carried out a comparative analysis of the total CCFs of the synthesized ensemble of signals with ensembles of orthogonal chirp and PCSK- signals used in the SAR aperture synthesis modes. The use of orthogonal chirp and PCSK-signals makes it possible to successfully suppress false signals from bright point targets from adjacent repetition periods. However, the high level of integral correlation noise of the CCF of orthogonal signals distorts radar images of weakly reflecting surfaces located next to bright extended objects. The indicators of the total correlation characteristics of the synthesized ensemble of signals exceed the corresponding indicators of the total correlation characteristics of the ensemble of orthogonal PCSK-and chirp signals, both without mismatch and with Doppler frequency mismatch. This makes it possible to successfully use an PCSK-signal with a zero autocorrelation zone to improve the quality of radar images and suppress recurrent range interference in SAR.