Signal Detection Sensitivity Research Articles

ГЕОДИНАМІКА

The purpose of the research was to verify the extraordinary big theoretical penetration depth of new developed georadar in the real conditions of karsts areas. The new kind of the Ground Penetrating Radar (GPR) – Roteg – was developed in 2013 (RTG-Tengler 2013). Its technical parameters (pulse peak on the transmitting antenna 20 kV or more, and the sensitivity of the receiving antenna at least 2 uV, i.e. the real signal detection level around 20 uV) express that the real signal detection sensitivity is 9 orders, i.e. 180 dB. Such sensitivity means that the real penetration depth should be two orders bigger than in the case of common GPR´s. We tested the real penetration depths in the suitable environmental conditions over the caves in the Moravian Karst and in Slovenia near Postojna cave. The measurements results showed that reflections from known caves are reliably detectable at depths of 40-210 m below the surface. Reflections from the geological structures up to a depth of 480 m, in which the bottom of Lift II is probably located, were observable on the profile above the Hranická abyss. The new kind of Roteg georadar with 20 kV pulses on the transmitting antennas was able to detect reflections from the Devon – Brno Granite contact at a depth of up to 850 m in the case of optimal conditions in the karst without soil cover (in the Malá Dohoda quarry in the Moravian Karst). The radarogram showed a change in the lithology between the Vilémovice – Lažánky limestone and layers of clasts in the bottom of limestone strata. Both of the tests mentioned above confirmed the extraordinary big penetration depth of the GPR signal which exceeded 500 m in karst conditions when using the maximum power on transmitting antennas. The quite new kind of GPR called Roteg with the extraordinary high voltage on transmitting antenna and pulse instead of harmonic signal generation of transmitted signal allows reach two orders bigger penetration depths than the common GPRs.. The new kind of Roteg GPR makes it possible to obtain data (especially from karst areas) from depths that were previously only accessible by seismic methods or boreholes. GPR measurements are orders of magnitude cheaper and much faster in the field.

Arrhythmia Detection using Pan-Tompkins Algorithm and Hilbert Transform with Real-Time ECG Signals

Electrocardiography (ECG) is a form of examination based on printing on millimetric paper the contraction and relaxation phases of the atria and ventricles of the heart, the electrical activity that occurs during the stimulation of the heart and the transmission of the stimulus. For the first diagnosis of diseases such as heart attacks, it is very important to make this preliminary impression and inform the doctor and patient as soon as possible.In this study, any arrhythmia risk can be determined by examining the heart rate and QRS width values calculated by graphing the signal value coming from the electrodes of the Electrocardiography monitor taken for heart rhythm monitoring in hospitals and by detecting the QRS points from the signal data with the Pan Tompkins algorithm and Hilbert Transform method. The image of the ECG graph and the signal analysis results, which are the result of the graphing of the data, can be sent via e-mail. In line with the values obtained in this study, it has been determined that the Pan Tompkins algorithm's signal detection accuracy, sensitivity and accurate prediction ratio gives better results than Hilbert Transform method during the detection of peaks from ECG signals.

Tailoring multifunctional gel for sensitive naked-eye and fluorescence dual-mode detection and effective adsorption of cadmium(Ⅱ) and lead(Ⅱ) ions in water

Water contamination caused by toxic cadmium (Cd2+) and lead (Pb2+) has become a global environmental issue, making it imperative to develop versatile functional materials for their monitoring and remediation. However, developing a versatile platform for the simultaneous sensitive multiple signal detection and effective removal of Cd2+ and Pb2+ remains a formidable challenge. Herein, a multifunctional gel for sensitive detection and effective adsorption of Cd2+ and Pb2+ is facilely constructed by integrating glycidyl methacrylate functionalized fluorescent graphene quantum dots and N, N'-methylenebisacrylamide co-crosslinked poly(thioctic acid) with physical crosslinked fluorescent gelatin. Interestingly, the gel shows cyan to chartreuse/violet fluorescence color change and fluorescent turn-on/turn-off switch after adsorbing Cd2+/Pb2+, thus for the first time simultaneous sensitive naked-eye and fluorescence dual-mode detection of Cd2+ and Pb2+ are achieved in a broad liner range (0.02–0.24 μmol). Meanwhile, the gel can effectively absorb Cd2+ and Pb2+ in water with maximum adsorption capacities of 194.553 and 259.067 mg g−1, respectively, surpassing previously reported adsorbents. Additionally, the gel exhibits excellent self-healing capability, reprocessability, biodegradability and biocompatibility, which can prolong its service life and prevent secondary contamination in practical applications. This study not only develops a versatile platform for the simultaneous sensitive detection and effective removal of Cd2+ and Pb2+, but also provides an innovative design strategy for inspiring future research to fabricate multifunctional materials for monitoring and decontamination heavy metal polluted water.

SARS-CoV-2 RNA Detection by a Cellphone-Based Amplification-Free System with CRISPR/CAS-Dependent Enzymatic (CASCADE) Assay.

CRISPR (Clustered regularly interspaced short palindromic repeats)‐based diagnostic technologies have emerged as a promising alternative to accelerate delivery of SARS‐CoV‐2 molecular detection at the point of need. However, efficient translation of CRISPR‐diagnostic technologies to field application is still hampered by dependence on target amplification and by reliance on fluorescence‐based results readout. Herein, an amplification‐free CRISPR/Cas12a‐based diagnostic technology for SARS‐CoV‐2 RNA detection is presented using a smartphone camera for results readout. This method, termed Cellphone‐based amplification‐free system with CRISPR/CAS‐dependent enzymatic (CASCADE) assay, relies on mobile phone imaging of a catalase‐generated gas bubble signal within a microfluidic channel and does not require any external hardware optical attachments. Upon specific detection of a SARS‐CoV‐2 reverse‐transcribed DNA/RNA heteroduplex target (orf1ab) by the ribonucleoprotein complex, the transcleavage collateral activity of the Cas12a protein on a Catalase:ssDNA probe triggers the bubble signal on the system. High analytical sensitivity in signal detection without previous target amplification (down to 50 copies µL−1) is observed in spiked samples, in ≈71 min from sample input to results readout. With the aid of a smartphone vision tool, high accuracy (AUC = 1.0; CI: 0.715 – 1.00) is achieved when the CASCADE system is tested with nasopharyngeal swab samples of PCR‐positive COVID‐19 patients.

Abstract LB013: Clinical validation of a targeted methylation-based multi-cancer early detection test

Abstract Introduction: A multi-cancer early detection (MCED) test as a complement to existing screening tests could increase the number of cancer cases detected in a population, potentially improving patient outcomes and survival as well as decreasing harmful and aggressive treatments. The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was designed to develop and validate a blood-based MCED test analyzing plasma cell-free DNA (cfDNA) to detect cancer signals across multiple cancer types and simultaneously predict their signal origin. Here, the results of the third and final pre-specified CCGA validation sub-study for a refined MCED test in a large cohort in preparation for clinical use are reported. Methods: CCGA is a prospective, multicenter, case-control, observational study with longitudinal follow-up (overall population N=15,254). In this sub-study (n=5309), key primary objectives were to evaluate test performance for cancer signal detection (specificity, overall sensitivity, sensitivity by clinical stage) and signal origin prediction (accuracy). cfDNA from evaluable samples was analyzed using a targeted methylation bisulfite sequencing assay and a machine learning algorithm. The classifier was trained to target a specificity of 99.4% and locked before analysis of the independent validation set. Overall, 4077 participants comprised the independent validation set with confirmed status (cancer: n=2823; non-cancer: n=1254 with non-cancer status confirmed at year-one follow-up). MCED test results are reported for this confirmed status set. Results: Mean (SD) age in the cancer and non-cancer groups was 62.6 (11.76) and 56.2 (12.63) years, respectively. Specificity for cancer signal detection was 99.5% (1248/1254; 95% confidence interval: 99.0-99.8%). Overall sensitivity for cancer signal detection was 51.5% (1453/2823; 49.6-53.3%); sensitivity increased with stage (Stage I: 16.8% [14.5-19.5%], Stage II: 40.4% [36.8-44.1%], Stage III: 77.0% [73.4-80.3%], Stage IV: 90.1% [87.5-92.2%]). Stage I-III sensitivity was 67.6% (593/877; 64.4-70.6%) in a pre-specified set of 12 high-signal cancers accounting for ~63% of annual US cancer deaths [1] and was 40.7% (863/2118; 38.7-42.9%) in all cancers. Cancer signals were detected across >50 cancer types [2]. Overall accuracy of signal origin prediction in true positives was 88.7% (87.0-90.2%). Conclusions: In this pre-specified, large-scale, clinical validation sub-study of CCGA, the MCED test detected cancer signals across >50 cancer types, which is critical to maximize the number of cancer cases detected in a population. This MCED test performed with high specificity and high accuracy of signal origin prediction. These data lay the foundation for population-scale clinical implementation of this test. 1.US Mortality Data 1969-2016 (www.seer.cancer.gov); based on 2015-2016. 2.Amin et al. CA Cancer J Clin. 2017;67:93e99. Citation Format: Eric A. Klein, Donald Richards, Allen Cohn, Mohan Tummala, Rosanna Lapham, David Cosgrove, Gina Chung, Jessica Clement, Jingjing Gao, Nathan Hunkapiller, Arash Jamshidi, Kathryn Kurtzman, Michael V. Seiden, Charles Swanton, Minetta C. Liu. Clinical validation of a targeted methylation-based multi-cancer early detection test [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB013.

Abstract LB058: Performance of a cell-free DNA-based multi-cancer detection test as a tool for diagnostic resolution of symptomatic cancers

Abstract Introduction: A test that detects cancer signal across multiple cancer types and predicts signal (tissue) origin (SO) could aid in more efficient diagnostic workup and shorten time to cancer diagnosis in individuals with signs and symptoms.Methods: The Circulating Cell-free Genome Atlas (CCGA; NCT02889978) study is a prospective, longitudinal, multicenter, case-control study to develop and validate a multi-cancer detection test. The 2nd CCGA substudy utilized a targeted methylation-based cell-free DNA assay and machine learning algorithm and included assessment of test performance (sensitivity and SO prediction accuracy) in a subgroup of participants with clinically presenting cancers (CPCs) that were undiagnosed prior to blood draw. Specificity was assessed in the noncancer group and subgroups with confounding (nonmalignant) conditions (CCs; eg, cirrhosis) and noncancer participants enrolled in hematology clinics (HCs).Results: Specificity was 99.5% (95% confidence interval: 98.2-99.9%; 396/398), 93.8% (71.7-99.7%; 15/16), and 99.3% (96.0-100.0%; 136/137) for the noncancer group, CCs subgroup, and HCs subgroup, respectively. Overall sensitivity among those with CPCs was 66.4% (62.2-70.3%; 344/518). Sensitivity of cancer signal detection increased with increasing clinical stage (Table). SO prediction accuracy was 91.7% (88.3-94.3%; 300/327) among CPC participants with cancers detected, excluding those with multiple or unknown primaries. The test demonstrated prognostic value as detected cancer participants had worse survival probability than those not detected. Conclusions: This multi-cancer detection test detected cancer signals and predicted SO in individuals with CPCs with high specificity. These findings support further clinical development of this multi-cancer detection test that could accelerate the diagnostic resolution of symptomatic cancers. Table. Sensitivity by Clinical Stage Across Cancer Type in Clinically Presenting CancersClinical StagePositive Test/Total Cancer; Sensitivity (95% CI)All*344/518; 66.4% (62.2-70.3%)I33/122; 27.0% (20.0-35.5%)II60/102; 58.8% (49.1-67.9%)III103/121; 85.1% (77.7-90.4%)IV136/147; 92.5% (87.1-95.8%)Not expected to be staged9/21; 42.9% (24.5-63.5%)Non-informative2/4; 50.0% (15.0-85.0%)CI, confidence interval.*One participant who had a positive test result had multiple primaries with clinical stage I and not-expected-to-be-staged. Citation Format: Alan H. Bryce, Minetta C. Liu, Michael V. Seiden, David D. Thiel, Donald Richards, Carlos Becerra, Kathryn N. Kurtzman, Xiaoji Chen, Tony Wu, Quan Zhang, Jingjing Gao, Nan Zhang, Earl Hubbell, Arash Jamshidi, Eric T. Fung, Eric A. Klein. Performance of a cell-free DNA-based multi-cancer detection test as a tool for diagnostic resolution of symptomatic cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB058.

Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set

A multi-cancer early detection (MCED) test used to complement existing screening could increase the number of cancers detected through population screening, potentially improving clinical outcomes. The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was a prospective, case-controlled, observational study and demonstrated that a blood-based MCED test utilizing cell-free DNA (cfDNA) sequencing in combination with machine learning could detect cancer signals across multiple cancer types and predict cancer signal origin (CSO) with high accuracy. The objective of this third and final CCGA substudy was to validate an MCED test version further refined for use as a screening tool. This pre-specified substudy included 4077 participants in an independent validation set (cancer: n= 2823; non-cancer: n= 1254, non-cancer status confirmed at year-one follow-up). Specificity, sensitivity, and CSO prediction accuracy were measured. Specificity for cancer signal detection was 99.5% [95% confidence interval (CI): 99.0% to 99.8%]. Overall sensitivity for cancer signal detection was 51.5% (49.6% to 53.3%); sensitivity increased with stage [stage I: 16.8% (14.5% to 19.5%), stage II: 40.4% (36.8% to 44.1%), stage III: 77.0% (73.4% to 80.3%), stage IV: 90.1% (87.5% to 92.2%)]. Stage I-III sensitivity was 67.6% (64.4% to 70.6%) in 12 pre-specified cancers that account for approximately two-thirds of annual USA cancer deaths and was 40.7% (38.7% to 42.9%) in all cancers. Cancer signals were detected across >50 cancer types. Overall accuracy of CSO prediction in true positives was 88.7% (87.0% to 90.2%). In this pre-specified, large-scale, clinical validation substudy, the MCED test demonstrated high specificity and accuracy of CSO prediction and detected cancer signals across a wide diversity of cancers. These results support the feasibility of this blood-based MCED test as a complement to existing single-cancer screening tests. NCT02889978.

Performance of a targeted methylation-based multi-cancer early detection test by race/ethnicity.

3071 Background: Disparities in cancer screening and outcomes based on factors such as gender, socioeconomic status, and race/ethnicity are well documented.1 The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was designed to develop and validate a blood-based multi-cancer early detection (MCED) test analyzing plasma cell-free DNA (cfDNA) to detect cancer signals across multiple cancer types and simultaneously predict cancer signal origin. Findings stratified by race/ethnicity from the third and final CCGA validation sub-study are reported. Methods: CCGA is a prospective, multicenter, case-control, observational study with longitudinal follow-up (overall N = 15,254). In this pre-specified exploratory analysis from the third substudy, key objectives were to evaluate test performance for cancer signal detection (specificity, overall sensitivity, and sensitivity by clinical stage) among racial/ethnic groups. Plasma cfDNA from evaluable samples was analyzed using a targeted methylation bisulfite sequencing assay and a machine learning approach. Overall, 4077 participants comprised the independent validation set with confirmed status (cancer: n = 2823; non-cancer: n = 1254). The groups stratified by race/ethnicity were White Non-Hispanic, Black Non-Hispanic, Other Non-Hispanic (including but not limited to Asian, Native Hawaiian, Pacific Islander, American Indian, Alaska Native), Hispanic (all races), and Other/unknown. The study was not powered to detect statistical differences between groups. Results: Cancer and non-cancer groups were predominantly White (2316/2823, 82.0% and 996/1254, 79.4%, respectively). Across racial/ethnic groups, specificity for cancer signal detection was 99.6% (White Non-Hispanic: 992/996, 95% confidence interval [99.0-99.8%]), 100.0% (Black Non-Hispanic: 85/85 [95.7-100.0%]), 100.0% (Other Non-Hispanic: 33/33 [89.6-100.0%]), 98.1% (Hispanic: 101/103 [93.2-99.5%]), and 100% (Other/unknown: 37/37 [90.6-100.0%]). Despite slight differences in cancer type and staging across racial/ethnic groups, overall sensitivity for cancer signal detection among groups ranged from 43.9% to 63.0% (White Non-Hispanic: 50.5%, 1169/2316 [48.4-52.5%], Black Non-Hispanic: 53.9%, 104/193 [46.8-60.8%], Other Non-Hispanic: 43.9%, 25/57 [31.8-56.7%], Hispanic: 63.0%, 121/192 [56.0-69.5%], and Other/unknown: 52.3%, 34/65 [40.4-64.0%]). For all racial/ethnic groups, sensitivity generally increased with clinical stage (with limited exceptions at Stage IV in some groups with small sample sizes). Conclusions: The MCED test demonstrated consistent specificity and sensitivity across racial/ethnic groups, though results are limited by sample size for some groups. These findings indicate broad applicability and support clinical implementation of this MCED test on a population scale. 1. Zavela et al. Brit J Cancer 2021. Clinical trial information: NCT02889978.

Measurement of Pulsation Strain at the Fingertip Using a Plastic FBG Sensor

There is a need to develop sensors that can measure vital signs such as pulse rate and blood pressure easily; such vital signs can be calculated by measuring arterial strain. To this end, a new method that uses a plastic fiber Bragg grating (FBG) sensor to measure pulsation strain at human fingertips is proposed herein. Plastic fiber has a smaller Young’s modulus than that of the glass fiber. Under the same applied pressure, the plastic FBG sensor deforms more than the silica glass FBG sensor, and the Bragg wavelength shift length becomes longer, which results in higher detection sensitivity. The pulsation strain of the fingertip was measured by the proposed method based on this characteristic. The most sensitive signal detection point in the fingertip was the measurement point 10 or 15 mm from the distal interphalangeal joint in the fingertip direction. Strain signals with periodic peaks were measured from five participants. From the strain signal measured with the cuff attached to the arm, the peak disappeared when the cuff moved, and it reappeared when the cuff was opened. Therefore, the measured signal waveform changes based on the blood flow condition. The pulse rate calculated from the peak interval of the measured signal showed high correlation with the reference pulse rate, and the measurement accuracy was ±5.5 bpm. It may be possible to calculate the blood pressure and pulse rate from the measurement signal of the pulsation strain of the fingertip obtained using the plastic FBG sensor.

Improved Prediction of Carbonless NMR Spectra by the Machine Learning of Theoretical and Fragment Descriptors for Environmental Mixture Analysis.

As the first multidimensional NMR approach, 2D J-resolved (2DJ) spectroscopy is distinguished by signal resolution and detection sensitivity with remarkable advantages for the exhaustive evaluation of complex mixtures and environmental samples due to its carbonless feature without the requirement of 13C connectivity. Generally, the 2DJ signal assignment of metabolic mixtures is problematic in spite of references to experimental NMR databases, owing to the existence of metabolic "dark matter." In this study, a new method to predict 2DJ spectra was developed with a combination of quantum mechanical (QM) computation and machine learning (ML). The predictive accuracy of J-coupling constants was evaluated using validated data. The root-mean-square deviation (RMSD) for QM computation was 3.52 Hz, while the RMSD for QM + ML was 1.21 Hz, indicating a substantial increase in predictive accuracy. The proposed model was applied to predict the 2DJ spectra of 60 standard substances and 55 components of seawater. Furthermore, two practical environmental samples were used to evaluate the robustness of the constructed predictive model. A J-coupling tree and J-split spectra produced from QM + ML of aliphatic moieties had good consistency with the experimental data, as compared with the theoretical data produced by QM computation. The predicted J-coupling tree for the J-coupling multiplet analysis of freely rotating bonds in the complex mixture, which is traditionally difficult, was interpretable. In addition, in silico identification of the J-split 1H NMR signals, which was independent of experimental databases, aided in the discovery of new components in a mixture.

In pursuit of sensitivity: Lessons learned from viral nucleic acid detection and quantification on the Raindance ddPCR platform.

Sensitive PCR detection of viral nucleic acids plays a critical role in infectious disease research, diagnosis and monitoring. In the context of SARS-CoV-2 detection, recent reports indicate that digital PCR-based tests are significantly more sensitive than traditional qPCR tests. Numerous factors can influence digital PCR reaction sensitivity. In this review, using a model for human HIV infection and the Raindance ddPCR platform as an example, we describe technical aspects that contribute to sensitive viral signal detection in DNA and RNA from tissue samples, which often harbor viral reservoirs and serve as better predictors of disease outcome and indicators of treatment efficacy.

Spherical-matching hyperbolic-array photoacoustic computed tomography.

Linear-array photoacoustic computed tomography (LA-PACT), for its flexibility and simplicity, has great potential in providing anatomical and functional information of tissues. However, the limited coverage view impedes the LA-PACT obtaining high-quality images. In this study, a photoacoustic tomographic system with a hyperbolic-array transducer was developed for stereoscopic PA imaging of carotid artery. The hyperbolic-array PACT increases the receiving sensitivity for PA signal detection due to its transducer's geometric structure matching with the spherical wave. The control phantom experiment shows that the proposed system can expand the angular coverage of ∼1/3 more than that of the LA-PACT system, and the volumetric PA images of rat's carotid artery demonstrates the potential of the system for carotid artery imaging. Furthermore, volumetric imaging of the human forearm verifies that the system has significant capability in human imaging, which indicates that it has bright prospect for assisting diagnosis in the vascular disease.

Emerging optofluidic technologies for biodiagnostic applications

AbstractThe unprecedented global COVID‐19 pandemic strongly argues the critical need for innovative diagnostic tools meeting the requirement of test speed, accuracy, and throughput. Owing to the integration of optics and microfluidics technologies, optofluidic technology enables highly precise flow manipulation and highly sensitive signal detection at the microscale, thus offering the promising potential for developing biodiagnostic applications. Research toward this direction is fast growing into an emerging area. In this paper, we give an overview of emerging optofluidic technologies for biodiagnostic applications with the focus on three common types of biomarkers: nucleic acid, protein, and cell. We conclude by discussing the challenges, opportunities, and future perspectives of this field.

A Polarity Alignment Method for Group-Averaging of Event-Related Neural Signals at Source Level in MEG Beamforming Applications.

Brain waveforms reconstructed at source level, like in beamforming, suffer polarity indeterminacy, which precludes direct group averaging of associated waveforms. We describe a polarity alignment method as an alternative of averaging rectified (i.e. absolute value) waveforms. Using MEG from an auditory localisation task, we compare the ability of the two approaches to enable signal detection in the primary auditory cortex over increasing sample size. The two methods are comparable in signal detection sensitivity, but the alignment method preserves group-average polarity alternation.

Сognitive Style Differences in Detection and Discrimination of Sensory Signals

The purpose of the study was to test individual differences in sensory sensitivity while performing signal detection and signal discrimination tasks. A total of 98 subjects performed two cognitive style tests on flexibility and rigidity of cognitive control, and focusing and scanning control, as well as two psychophysical tasks on visual signal detection (“yes/no” method) and loudness discrimination (“same/different”), each including two difficulty levels. Task type and difficulty level were considered as stimulation factors, and cognitive styles were considered as individual differences factors. The effects of both cognitive styles along with the effect of their interaction were revealed. ‘Flexible’ subjects and ‘scanners’ showed higher sensitivity in signal detection compared to ‘rigid’ subjects and ‘focusers’, respectively. Whereas no between-group differences were found in the accuracy of signal discrimination. Thus, we revealed individual differences in sensitivity, driven by cognitive style characteristics on the one hand, and task type on the other.

Weak Radio-Frequency Signal Detection Based on Piezo-Opto-Electro-Mechanical System: Architecture Design and Sensitivity Prediction

We propose a novel radio-frequency (RF) receiving architecture based on the microelectromechanical system (MEMS) and optical coherent detection module. The architecture converts the received electrical signal into mechanical vibration through the piezoelectric effect and adopts an optical detection module to detect the mechanical vibration. We analyze the response function of the piezoelectric film to an RF signal, the noise-limited sensitivity of the optical detection module, and the system transfer function in the frequency domain. A simple on–off keying (OOK) modulation with carrier frequency 1 GHz is used to numerically evaluate the detection sensitivity. The result shows that considering the main noise sources in system, the signal detection sensitivity can reach around −116.6 dBm at bandwidth 5 MHz. Such sensitivity significantly outperforms that of the currently deployed long-term evolution (LTE) system. Finally, we conduct preliminary experiments based on an off-the-shelf aluminium nitride piezoelectric film, which shows that the vibration amplitude based on the theoretical prediction can be achieved.

Improvement of Sensitivity of Signal Detection in Stochastic Resonance Using Oversampling

Improvement of Sensitivity of Signal Detection in Stochastic Resonance Using Oversampling

Improved Signal Detection Sensitivity for High Resolution Imaging in Scanning Acoustic Tomography

Abstract Scanning acoustic tomography (SAT) is widely used to detect defects such as voids and delamination in electronic devices. In this article, the authors explain how they improved the spatial resolution and detection sensitivity of SAT by switching from a conventional piezoelectric probe to a capacitive micromachined ultrasound transducer (CMUT) and by using pulse compression signal processing. They also present examples showing how the improvement makes it possible to detect very small defects in multilayer stacks and BGA packages whether in through-transmission or reflection imaging mode.

Early signal detection of adverse events following influenza vaccination using proportional reporting ratio, Victoria, Australia.

IntroductionTimely adverse event following immunisation (AEFI) signal event detection is essential to minimise further vaccinees receiving unsafe vaccines. We explored the proportional reporting ratio (PRR) ability to detect two known signal events with influenza vaccines with the aim of providing a model for prospective routine signal detection and improving vaccine safety surveillance in Australia.MethodsPassive AEFI surveillance reports from 2008–2017 relating to influenza vaccines were accessed from the Australian SAEFVIC (Victoria) database. Proportional reporting ratios were calculated for two vaccine-event categories; fever and allergic AEFI. Signal detection sensitivity for two known signal events were determined using weekly data; cumulative data by individual year and; cumulative for all previous years. Signal event thresholds of PRR ≥2 and Chi-square ≥4 were applied.ResultsPRR provided sensitive signal detection when calculated cumulatively by individual year or by all previous years. Known signal events were detected 15 and 11 days earlier than traditional methods used at the time of the actual events.ConclusionUtilising a single jurisdiction’s data, PRR improved vaccine pharmacovigilance and showed the potential to detect important safety signals much earlier than previously. It has potential to maximise immunisation safety in Australia. This study progresses the necessary work to establish national cohesion for passive surveillance signal detection and strengthen routine Australian vaccine pharmacovigilance.

Single-cell analysis for drug development using convex lens-induced confinement imaging.

New technologies have powered rapid advances in cellular imaging, genomics and phenotypic analysis in life sciences. However, most of these methods operate at sample population levels and provide statistical averages of aggregated data that fail to capture single-cell heterogeneity, complicating drug discovery and development. Here we demonstrate a new single-cell approach based on convex lens-induced confinement (CLiC) microscopy. We validated CLiC on yeast cells, demonstrating subcellular localization with an enhanced signal-to-noise and fluorescent signal detection sensitivity compared with traditional imaging. In the live-cell CLiC assay, cellular proliferation times were consistent with flask culture. Using methotrexate, we provide drug response data showing a fivefold cell size increase following drug exposure. Taken together, CLiC enables high-quality imaging of single-cell drug response and proliferation for extended observation periods.