Non-target Signals Research Articles

An ultrasensitive and specific fluorescence split-aptasensor for VEGF165 detection based on nicking enzyme-assisted 3D DNA walker coupling with CRISPR-Cas12a

The overexpression of vascular endothelial growth factor 165 (VEGF165) in cancer cells plays a pivotal role in promoting tumor metastasis by facilitating their excessively rapid proliferation and division. Hence, the development of analytical methods possessing high sensitivity and resistance to interference is imperative for the detection of VEGF165. Various types of aptasensors have been devised for VEGF165 detection; however, the performance of these biosensors can be influenced by non-target signals caused by conformational changes in unbound aptamers. The paper shows the creation of a precise and sensitive fluorescence biosensor designed to detect VEGF165 by using a VEGF165-specific split aptamer. Additionally, this biosensor employs nicking enzyme-assisted DNA walker coupling with CRISPR-Cas12a to achieve dual-signal amplification. The VEGF165 calibration curve shows a detection limit of 268 fM and has a broad linear range from 5 to 4000 nM. The fluorometric biosensor was utilized to detect VEGF165 in human serum and cellular homogenate samples, yielding good outcomes. The innovative design serves as proof of concept and demonstrates significant potential in detecting various targets.

An inadequate sampling of the soundscape leads to over-optimistic estimates of recogniser performance: a case study of two sympatric macaw species

ABSTRACT Passive acoustic monitoring (PAM) – autonomously recording ambient sound – could dramatically increase the scale and robustness of species monitoring in rainforest ecosystems. PAM generates large volumes of data that require automated methods of target species detection. Species-specific recognisers, which often use supervised machine learning, can achieve this goal. However, they require a large training dataset of target and non-target signals, which is time-consuming and challenging to create. Unfortunately, very little information about creating training datasets for supervised machine learning recognisers is available, especially for tropical ecosystems. Here, we show an iterative approach to creating a training dataset that improved recogniser precision from 0.12 to 0.55. By sampling background noise using an initial small recogniser, we can address one of the significant challenges of training dataset creation in acoustically diverse environments. Our work demonstrates that recognisers will likely fail in real-world settings unless the training dataset size is large enough and sufficiently representative of the ambient soundscape. We outline a workflow that can provide users with an accessible way to create a species-specific PAM recogniser that addresses these issues for tropical rainforest environments. Our work provides important lessons for PAM practitioners wanting to develop species-specific recognisers for acoustically diverse ecosystems.

HIDR: A Hierarchical Independent Detection and Recognition strategy for underwater acoustic multi-target recognition

This paper proposed HIDR (Hierarchical Independent Detection and Recognition), a novel strategy for underwater acoustic multi-target recognition. HIDR adopts a hierarchical architecture instead of the single-step traditional target recognition task, which is a relatively challenging task especially for the multi-target recognition. HIDR would firstly distinguish target signals from non-target signals as a detection stage, then pinpoint the specific target from all possible types of target signals as a recognition stage. This hierarchical architecture cannot only sharply reduce the difficulty of feature extraction, but also benefit for the higher recognition accuracy. A universal feature extraction strategy is utilized to extract two independent feature sets for the two stages respectively, which include auditory parameters especially adaptive to detection/recognition stages, as well as other common time/frequency domain features. Experiments with real sea trial data are conducted to compare HIDR with existing high-quality multi-target recognition schemes. Experimental results show that the proposed strategy can reach up to 88.71% of recognition accuracy in four-class recognition task, which is more than 5.25% higher than that of mainstream methods.

Deep Learning Depends on Segregated Dendritic Compartments and Spike Frequency Adaptation

Deep learning mimics the function of the biological nervous system via adopting strategies motivated by neurophysiology. However, integrating biophysical characteristics to make deep learning more brain-like remains challenging. To investigate whether the more realistic simulated neurons could make deep learning more realistic from the biology perspective, we propose a three-layer multi-compartment deep learning network with spike frequency adaptation (SFA). We utilize the segregated apical dendrites in the hidden layer to implement the credit assignment and coordinate the distribution of information flow. The introduction of SFA serves as the teaching signal. The results show that the apical dendrites separate the feedforward and feedback signals and instruct the credit assignment problem. The apical dendrites diverse the firing types of target neurons and further decrease the firing rate of non-target neurons via switching their firing types from the tonic firing type to predominantly burst firing. Furthermore, the expression of SFA in the non-target neurons further amplifies the difference between the firing rate of target and non-target signals. This work provides new insight into a deep learning network with more biophysical neurons and is a meaningful supplementary for deep learning.

Sound pressure level real-time monitoring of outdoor directional noise

A single microphone can’t select an efficient target signal from a mixed sound source when measuring the sound pressure level (SPL) of a target noise source in an outdoor environment. Therefore, a microphone array is used to enhance the target signal through beamforming to restrain the interference of non-target signals effectively. Owing to their advantages of small size and low price, micro-electro-mechanical system (MEMS) microphone arrays are widely used. However, MEMS microphone mismatch commonly occurs due to production tolerances as well as aging effects. To calibrate the SPL measurement difference caused by the whole mismatch microphone array when outdoor noise monitoring is performed, a real-time calibration method was introduced in this study by compensating for the SPL difference between the reference microphone and MEMS microphone array. The circular harmonics domain-multiple signal classification sound source localization algorithm was utilized to select the effective target signal component in the time–frequency domain, and the standard transfer functions of the reference microphone were utilized to correct the SPL measurement bias of the array. To verify the feasibility of the method, the consistency of the compensating SPL difference between multi-noise and single-target noise environments was experimentally investigated. The calibration method exhibited good accuracy and robustness based on the results and can also be applied to spherical microphone array noise monitoring device.

Assessing the Effectiveness of Generalized Likelihood Ratio Test Detector Schemes in Seismic Event Detection and the Avoidance of Nontarget Signals

ABSTRACT Cross-correlation techniques have played a long-standing and pivotal role in seismic event monitoring. However, the performance of correlation-based detectors is challenged by nuisance seismicity, or nontarget signals. Such detections are a problem when the mission is to automatically map events to the correct source region. Using aftershocks of the 2014 Mw 6.0 South Napa, California, earthquake, we demonstrate the effectiveness of utilizing a dynamic correlation processor framework in a generalized likelihood ratio test (GLRT) detector configuration to minimize nontarget detections. A GLRT maximizes a detection statistic with respect to one or more unknown parameters. In this case, the detection statistic is a template signal match against the waveform in a window sliding over a data stream, and the unknown parameter is an index variable indicating group membership of the template event or events. Detected events are assigned to the event group that yields the largest detection statistic. Our results show that a GLRT detector will outperform a suite of independently operating correlation and subspace detectors in terms of having a lower nontarget detection rate at a given missed detection rate. We also show that a GLRT detector composed of a few high-rank subspace detectors has a slightly higher nontarget detection rate, but a significantly lower missed detection rate, than a GLRT detector composed of many low-rank subspace detectors. The high-rank GLRT configuration produced impressive results even with marginal data (single channel, single station, and very low time bandwidth product), which bodes well for the utility of building efficient aftershock classification systems and global monitoring systems at larger scales. However, future work is required to assess performance at the regional scale and to assess the performance of the system at detecting target events not used in the detector template creation.

A deep neural network classifier for P300 BCI speller based on Cohen’s class time-frequency distribution

This paper presents a new method of predicting the P300 component of an electroencephalography (EEG)signal to recognize the characters in a P300 brain-computer interface (BCI) speller accurately. This method consistsof a deep learning model and the nonlinear time-frequency features. It is believed that the combination of the deepmodel network and extracting the nonlinear features of the EEG led this research to a better prediction of the P300and, therefore, character recognition. Cohen's class distribution is used in order to extract the nonlinear features of theEEG. Evaluating all of the kernels, Butterworth found to be more informative and it produced better results. Basedon the differences observed between time-frequency responses of target and nontarget signals, specific subbands areselected to extract seven features. A deep-structured neural network, namely stacked sparse autoencoders, is appliedfor BCI character recognition. This deep network reduces the dimension of feature space by extracting unsupervisedfeatures. Then, the features are fed to a Softmax classifier. Afterward, the whole network passes a fine-tuning phase by asupervised backpropagation algorithm. For evaluating the work, Dataset II of BCI Competition III is utilized. Based onthe results, this approach would improve the accuracy in both P300 detection and character recognition. This researchresults in 82.7% and 93.5% accuracy for P300 classification and character recognition, respectively.

Identification of LC-HRMS nontarget signals in groundwater after source related prioritization

Groundwater is a major drinking water resource but its quality with regard to organic micropollutants (MPs) is insufficiently assessed. Therefore, we aimed to investigate Swiss groundwater more comprehensively using liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). First, samples from 60 sites were classified as having high or low urban or agricultural influence based on 498 target compounds associated with either urban or agricultural sources. Second, all LC-HRMS signals were related to their potential origin (urban, urban and agricultural, agricultural, or not classifiable) based on their occurrence and intensity in the classified samples. A considerable fraction of estimated concentrations associated with urban and/or agricultural sources could not be explained by the 139 detected targets. The most intense nontarget signals were automatically annotated with structure proposals using MetFrag and SIRIUS4/CSI:FingerID with a list of >988,000 compounds. Additionally, suspect screening was performed for 1162 compounds with predicted high groundwater mobility from primarily urban sources. Finally, 12 nontargets and 11 suspects were identified unequivocally (Level 1), while 17 further compounds were tentatively identified (Level 2a/3). amongst these were 13 pollutants thus far not reported in groundwater, such as: the industrial chemicals 2,5-dichlorobenzenesulfonic acid (19 detections, up to 100 ng L−1), phenylphosponic acid (10 detections, up to 50 ng L−1), triisopropanolamine borate (2 detections, up to 40 ng L−1), O-des[2-aminoethyl]-O-carboxymethyl dehydroamlodipine, a transformation product (TP) of the blood pressure regulator amlodipine (17 detections), and the TP SYN542490 of the herbicide metolachlor (Level 3, 33 detections, estimated concentrations up to 100–500 ng L−1). One monitoring site was far more contaminated than other sites based on estimated total concentrations of potential MPs, which was supported by the elucidation of site-specific nontarget signals such as the carcinogen chlorendic acid, and various naphthalenedisulfonic acids. Many compounds remained unknown, but overall, source related prioritisation proved an effective approach to support identification of compounds in groundwater.

Development and optimization of a simian immunodeficiency virus (SIV) droplet digital PCR (ddPCR) assay.

Accurate and sensitive quantification of rebound competent HIV that persists despite combination antiretroviral treatment (cART), including in latently infected cells (i.e., viral reservoir), is critical for evaluating cure strategies for decreasing or eliminating this reservoir. Simian immunodeficiency virus (SIV)-infected Rhesus macaques are an important non-human primate (NHP) system for studying potential cure strategies as they model many key aspects of human HIV-infection including the persistence of a latent viral reservoir in resting memory CD4+ T cells in animals receiving prolonged cART. In this report, we describe the design and testing of a sensitive SIV droplet digital PCR (ddPCR) assay through exploring the combination and optimization of different probe systems (including single, double quencher probes and minor groove binder (MGB) probes) and reaction conditions to eliminate background signal(s), ensure distinct target signal cluster separation from non-target signals, and enable detection and quantification of low level authentic target signals. Similar reaction conditions and assay validation procedures can be explored for potential development of additional assays for other applications that require sensitive detection of low-level targets in a large background of nucleic acid input derived from cell or tissue sources.

A split aptamer (SPA)-based sandwich-type biosensor for facile and rapid detection of streptomycin

As antibiotic pollution is gaining prominence as a global issue, the demand for detection of streptomycin (STR), which is a widely used antibiotic with potential human health and ecological risks, has attracted increasing attention. Aptamer-based biosensors have been developed for the detection of STR in buffers and samples, however, the non-target signals due to the conformational variation of free aptamers possibly affect their sensitivity and stability. In this study, by introducing the STR-specific split aptamer (SPA), a sensitive evanescent wave fluorescent (EWF) biosensor is developed for the sandwich-type based detection of STR. The standard calibration curve obtained for STR has a detection limit of 33 nM with a linear range of 60–526 nM. This biosensor exhibited good selectivity, reliable reusability for at least 100 times measurements, and high recovery rates for spiked water samples; moreover, all detection steps are easy-to-operate and can be completed in 5 min. Therefore, it exhibits great promise for actual on-site environmental monitoring. Additionally, without introducing any other oligonucleotides or auxiliary materials, this SPA-based biosensing method shows potential as a simple, sensitive, and low-cost manner for the detection of other small molecular targets.

Deep Learning Based Target Cancellation for Speech Dereverberation.

This study investigates deep learning based single- and multi-channel speech dereverberation. For single-channel processing, we extend magnitude-domain masking and mapping based dereverberation to complex-domain mapping, where deep neural networks (DNNs) are trained to predict the real and imaginary (RI) components of the direct-path signal from reverberant (and noisy) ones. For multi-channel processing, we first compute a minimum variance distortionless response (MVDR) beamformer to cancel the direct-path signal, and then feed the RI components of the cancelled signal, which is expected to be a filtered version of non-target signals, as additional features to perform dereverberation. Trained on a large dataset of simulated room impulse responses, our models show excellent speech dereverberation and recognition performance on the test set of the REVERB challenge, consistently better than single- and multi-channel weighted prediction error (WPE) algorithms.

P300 characterization using empirical mode decomposition

As the communication between brain and computer becomes more accessible the extraction of important features of electrophysiological signals is an essential step in artificial communication systems. This paper proposes the usage of the Empirical Mode Decomposition to identify characteristics of the P300 signal and classify target and non-target signals using a feedforward neural network. The results show that through the usage of EMD method it is possible to identify the P300 signal using low volume of data.

Effects of Cognitive Loading on the Development of Muscle Fatigue

The aim of the present work was to study the effects of cognitive loading requiring activation of the attention systems on the development of muscle fatigue. The dynamics of measures of arm muscle fatigue were evaluated in two sessions, separated by 5 min, consisting of 250 maximum-strength dynamometer compressions in response to acoustic target signals in three tasks with different levels of cognitive loading. The first experiment used only the target signals; in experiment two, the subjects had to discriminate the target signal from an equal number (250) of nontarget signals, which required voluntary attention. In experiment three, the proportions of target stimuli were 17 and 83%, respectively, i.e., the target stimulus was random (deviant), activating not only voluntary attention, but also involuntary attention. Measurements of grip strength, maximum voluntary contraction strength, and subjective assessments of fatigue before and after the motor tasks showed that experiments involving voluntary and involuntary attention to sound stimuli produced a smaller rate of development of muscle fatigue. Activation of involuntary attention, accompanied by the appearance of mismatch negativity on the EEG, enhanced the effect of voluntary attention.

Fast Threshold Image Segmentation Based on 2D Fuzzy Fisher and Random Local Optimized QPSO.

In the paper, a real-time segmentation method that separates the target signal from the navigation image is proposed. In the approaching docking stage, the navigation image is composed of target and non-target signal, which are separately bright spot and space vehicle itself. Since the non-target signals is the main part of the navigation image, the traditional entropy-related criterions and Ostu-related criterions will bring inadequate segmentation, while the mere 2D Fisher criterion will causes over-segmentation, all the methods show their shortages in dealing with this kind of case. To guarantee a precise image segmentation, a revised 2D fuzzy Fisher is proposed in the paper to make a trade-off between positioning target regions and retaining target fuzzy boundaries. First, to reduce redundant computations in finding the threshold pair, a 2D fuzzy Fisher criterion-based integral image is established by way of simplifying the corresponding fuzzy domains. Then, to quicken the convergence, a random orthogonal component is added in its quasi-optimum particle to enhance its local searching capacity in each iteration. Experimental results show its competence of quick segmentation.

Threshold Magnitudes for a Multichannel Correlation Detector in Background Seismicity

Colocated explosive sources often produce correlated seismic waveforms. Multichannel correlation detectors identify these signals by scanning template waveforms recorded from known reference events against data to find similar waveforms. This screening problem is challenged at thresholds required to monitor smaller explosions, often because non-target signals falsely trigger such detectors. Therefore, it is generally unclear what thresholds will reliably identify a target explosion while screening non-target background seismicity. Here, we estimate threshold magnitudes for hypothetical explosions located at the North Korean nuclear test site over six months of 2010, by processing International Monitoring System (IMS) array data with a multichannel waveform correlation detector. Our method (1) accounts for low amplitude background seismicity that falsely triggers correlation detectors but is unidentifiable with conventional power beams, (2) adapts to diurnally variable noise levels and (3) uses source-receiver reciprocity concepts to estimate thresholds for explosions spatially separated from the template source. Furthermore, we find that underground explosions with body wave magnitudes mb = 1.66 are detectable at the IMS array USRK with probability 0.99, when using template waveforms consisting only of P -waves, without false alarms. We conservatively find that these thresholds also increase by up to a magnitude unit for sources locatedmore » 4 km or more from the Feb.12, 2013 announced nuclear test.« less

Картирование мозга при восприятии целевой и нецелевой акустической информации

In this paper the relationships between of middle- and long-latency acoustical evoked potentials on model of selective attention are estimated. Topographical brain mapping in this situation is model for approximation of direct and back influences in neural networks at processing the acoustic information. The mechanisms of perception of target and non-target acoustic signals in the brain on various epoch of the analysis are estimated.

SEPERATION OF IKONOS SENSOR’S ELECTRONIC NOISE FROM ATMOSPHERIC INDUCED EFFECTS

Abstract. The quality of satellite images has always been of particular importance in remote sensing. Signals received from satellite sensors include some signals other than those of target signal that may be classified totally as the atmospheric effect and the sensor induced noise. Separating non-target signals and attempting in removing them from images is essential. One method for measuring and removing non-target signals is that of atmospheric correction by Dark Object Subtraction (DOS). This method is based on the sensor’s output for the targets that should have almost zero reflectance in a given band. Next, the obtained value will be deducted from the remaining pixels values; regardless of the type of the sensors. Each Charge-Coupled Device (CCD) has its own noise behavior; therefore, the amount deducted values from each pixel can be different for each CCD unit and type. Among the various noises of the CCD and their related electronic circuits, dark current noise, non-uniform pixels noise and read noise were selected to be studied in this paper. The data were obtained from multispectral sensor images of IKONOS. This sensor can provide images in two forms of Panchromatic (PAN) and Multispectral (MS). The results of this study showed that the amount of dark object pixels and the total amount of CCD noises in each band are different. Separation of the noises introduced in this paper from the amount of dark object pixel values can result in an upgraded method for image atmosphere corrections.

Neurophysiological characteristics of cognitive functions in patients with first episodes of endogenous psychosis

This report presents studies of the neurophysiological correlates of the characteristics of cognitive disorders in patients with first psychotic episodes of endogenous psychosis at juvenile age. Three groups of patients were studied: those with a predominance of catatonic symptomatology (22 patients), those with a predominance of hallucinatory-delusional symptomatology (22 patients), and those with a predominance of affective-delusional symptomatology (24 patients), along with a group of psychologically healthy subjects (15 subjects). Parameters of auditory evoked potentials were analyzed using the oddball paradigm. The group with a predominance of catatonic disorders showed the greatest differences in the latent periods (LP) of the N200 and P300 components as compared with the other groups; patients with a predominance of hallucinatory-delusional symptomatology showed the most localized anomalies in the latent period of the P300 component; the group of patients dominated by affective-delusional symptomatology showed almost no increase in the latent period of the N200 component, though the extents of anomalies in the N100 component in responses to non-target signals and deviations in the P300 component were more marked than in the other groups. These characteristics of the neurophysiological correlates of cognitive functions in each group of patients supported the significance of evaluating the psychopathological structure of manifest psychotic episodes for determining the clinical typology.

Assessment of 18F PET signals for automatic target volume definition in radiotherapy treatment planning

Introduction Positron emission tomography (PET) alone or in combination with computer tomography (PET/CT) is increasingly used in target volume assessment. A standardized way of converting PET signals into target volumes is not available at present. Materials and methods Assuming a uniform signal emission from a tumour and surrounding normal tissues, a model-based method was developed to determine a relative threshold level (Th rel) for gross tumour volume delineation. Two phantoms consisting of cylindrical and spherical sources of diameter ranging from 4.5 to 43 mm in a tank and 18F activities ranging from 0.001 to 0.15 MBq/ml for tank and sources, respectively, were used for PET/CT imaging. A Th rel was calculated that best corresponded to the physical diameter of the cylindrical sources. Software (SW) was generated to automatically delineate volumes based on this threshold. The SW was validated for in vitro and in vivo PET signals. Results The Th rel best representing the source diameter was 41 ± 2.5% (95% confidence level) of the background-subtracted signal. The mean deviation for sources of diameter ⩾12.5 mm was ⩽1.5 mm. The Th rel was constant for diameters ⩾12.5 mm. For source diameters <12.5 mm, the 41% level over-estimated the real source diameter by a factor depending on the diameter. In an in vitro set-up the SW was capable of segmenting solitary PET volumes to within 1.4 mm (1SD). For non-homogeneous signals in a clinical set-up minimal manual intervention is presently required to separate target from non-target signals. The SW may slightly underestimate target volumes when compared with CT-based volumes, but works well as a first approximation. The volume can be manually adapted to give the ultimate target volume. Conclusions SW-based automatic delineation of the volume of 18F activity is feasible and highly reproducible. Volumes can be subsequently modified by the clinician if necessary. This approach will increase the efficiency of the planning process.

Mental chronometry of target detection: human thalamus leads cortex

Attentive monitoring of environmental stimuli is most fundamental for rapid target detection. The aim of this study was to assess the timing of thalamic versus cortical processes involved in this cognitive operation. To this end, simultaneous depth and scalp EEG was recorded in eight patients with essential tremor, undergoing thalamic deep brain stimulation (DBS), when the DBS electrodes could be accessed via their temporarily externalized leads. The patients performed an oddball task consisting of 300 presentations of one frequent and two rare visual cues, appearing in randomized order. One of the rare cues was defined as a target, the occurrences of which had to be indicated by a button press (motor condition) or silently counted (non-motor condition). At the scalp and the thalamus, event-related potentials (ERP) were largest upon target presentation, with peak latencies in the time domain of classical P300 responses. Remarkably, target-specific thalamic ERP emerged significantly prior to scalp P300. Furthermore, whereas scalp ERP had a higher amplitude upon rare than upon frequent non-target signals, thalamic ERP were independent of stimulus probability. This pattern was identified during motor and non-motor task execution. We conclude that the human thalamus specifically supports the early recognition of target events and can widely distribute this label through its divergent cortical projections.