Signal Correlation Research Articles

A Novel FSS Loaded Circular Slot Monopole Antenna for Microwave Breast Imaging Application

Breast cancer continues to be a major contributor to women's global mortality rates, emphasizing the need to explore alternative and supplementary early-stage detection techniques. This study delves into a circular slot monopole ultra-wideband (UWB) antenna equipped with a frequency-selective surface (CSMA-FSS) for early detection microwave imaging applications. To expand the impedance bandwidth of the traditional circular monopole antenna, a circular slit is introduced on a monopole structure. Additionally, a 10 × 10 unit cell FSS structure is integrated into the antenna as a far-field reflecting plane to enhance gain characteristics. The antenna, crafted with dimensions of 50 × 50 × 1.6 mm³ and a 1.6 mm thick FR4 substrate, operates across the ultra-wideband frequency spectrum, spanning from 3.1 GHz to 12 GHz, while consistently maintaining a reflection coefficient (S 11) value of – 10 dB or lower. Notably, the antenna showcases a gain of over 5 dBi across the entire 3.1 GHz to 12 GHz frequency range. The suggested antenna is ideal for microwave imaging applications due to its broad impedance bandwidth, impressive gain, and excellent directivity. Furthermore, it maintains a relatively uniform group delay, with a deviation of ±0.5 ns throughout the UWB band. In the time domain characteristics, the antenna exhibits excellent pulse handling capabilities and strong signal correlation, featuring an S 21 value of less than – 25 dB. The characteristics, observed in both the time and frequency domains, suggest that the proposed antenna holds significant potential for substantially enhancing breast cancer imaging system.

Discrepant Phenotyping of Monocytes Based on CX3CR1 and CCR2 Using Fluorescent Reporters and Antibodies.

Monocytes, as well as downstream macrophages and dendritic cells, are essential players in the immune system, fulfilling key roles in homeostasis as well as in inflammatory conditions. Conventionally, driven by studies on reporter models, mouse monocytes are categorized into a classical and a non-classical subset based on their inversely correlated surface expression of Ly6C/CCR2 and CX3CR1. Here, we aimed to challenge this concept by antibody staining and reporter mouse models. Therefore, we took advantage of Cx3cr1GFP and Ccr2RFP reporter mice, in which the respective gene was replaced by a fluorescent reporter protein gene. We analyzed the expression of CX3CR1 and CCR2 by flow cytometry using several validated fluorochrome-coupled antibodies and compared them with the reporter gene signal in these reporter mouse strains. Although we were able to validate the specificity of the fluorochrome-coupled flow cytometry antibodies, mouse Ly6Chigh classical and Ly6Clow non-classical monocytes showed no differences in CX3CR1 expression levels in the peripheral blood and spleen when stained with these antibodies. On the contrary, in Cx3cr1GFP reporter mice, we were able to reproduce the inverse correlation of the CX3CR1 reporter gene signal and Ly6C surface expression. Furthermore, differential CCR2 surface expression correlating with the expression of Ly6C was observed by antibody staining, but not in Ccr2RFP reporter mice. In conclusion, our data suggest that phenotyping strategies for mouse monocyte subsets should be carefully selected. In accordance with the literature, the suitability of CX3CR1 antibody staining is limited, whereas for CCR2, caution should be applied when using reporter mice.

Abstract PO5-22-09: Positive pegulicianine fluorescence rate in the lumpectomy cavity correlates with tumor distance to margins in excised tissue

Abstract Title: Positive pegulicianine fluorescence rate in the lumpectomy cavity correlates with tumor distance to margins in excised tissue Background: A randomized, two-arm pivotal study of pegulicianine fluorescence guided surgery (pFGS) at 14 US sites was conducted using the Lumicell Direct Visualization System (DVS) to identify residual cancer in the lumpectomy cavity after the standard of care (SoC) tumor excision. We reported that the Lumicell DVS detects residual cancer in the lumpectomy cavity, including in orientations pathologically negative on the SoC specimen. In addition, 15% (9 of 62) of patients with positive SoC margins were converted to final negative margins by taking Lumicell DVS directed cavity margins, avoiding a second unnecessary surgery. Interestingly, 8 of these 9 patients had no tumor found in the Lumicell DVS guided specimens. We hypothesize that fluorescence decreases as the distance from the tumor boundary increases. We analyzed data from the pivotal study (NCT03686215) to assess correlation of pegulicianine signal with tumor-to-margin distance, and its potential clinical value. Methods: The pivotal study enrolled 406 patients undergoing breast conserving surgery (BCS) for stage 0-III breast cancers. Patients received an IV injection of pegulicianine 2-6 hours prior to surgery. After the standard of care lumpectomy was completed, additional Lumicell DVS-guided cavity margins were excised at sites of positive pegulicianine fluorescence signal in the lumpectomy cavity walls using a hand-held imaging device and patient-calibrated cancer detection software. The positive fluorescence rate, that is, the rate at which the Lumicell DVS indicates areas suspected to contain cancer, was calculated as the ratio of orientations with positive pegulicianine signal to the total number of orientations with (1) negative margins, (2) positive margins and (3) positive margins with ink on tumor, on the initial SoC lumpectomy specimen. Calculations were made on a per-margin basis. Positive margins were defined as ink on tumor for invasive cancer and tumor less than 2 mm from the margin for ductal carcinoma in situ (DCIS) alone. Results: Results are presented in Table 1. We found a statistically significant higher positive fluorescence rate in orientations with positive margins (p=0.044). The positive fluorescence rate was even higher (21.1%) when only considering cases with ink on tumor (a subset of the positive margin group). Discussion: By design, pegulicianine is activated by enzymes within the tumor and in stromal cells at the invasive front surrounding the tumor. Thus, when tumor is closer to the edge of the lumpectomy specimen, the rate of positive pegulicianine fluorescence in the cavity increases, being highest when there is tumor present at the inked margin. This feature of pegulicianine fluorescence guides the surgeon to resect more tissue in these areas in order to achieve a negative margin of the required width. The conversion of positive margins after the SoC lumpectomy to appropriately wide final negative margins prevents a second surgery, even when there is no tumor in the DVS guided margins. These findings have strengthened our understanding of the utility of this technology. Table 1 Positive cavity wall fluorescence rate for SoC lumpectomy positive margins, SoC lumpectomy negative margins and SoC lumpectomy ink on tumor margins Citation Format: Irene Wapnir, E Shelley Hwang, Kelly Hunt, David Carr, Peter Blumencranz, Manna Chang, Kate Smith, Jorge Ferrer, Barbara Smith. Positive pegulicianine fluorescence rate in the lumpectomy cavity correlates with tumor distance to margins in excised tissue [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-22-09.

Weak signal detection technique based on Durbin-Watson test and one-bit sampling.

Correlation-based detection techniques are widely used in the weak periodic signal detection field. Traditionally, they are based on extracting the correlation of a weak signal from noise. Considering the impact of a weak signal on the randomness of background noise, this article takes the opposite approach and proposes a weak signal detection technique based on the Durbin-Watson (DW) test and one-bit sampling, detecting the weak signal due to the extent to which the randomness of noise is affected. The randomness of noise is analyzed through the DW test, which is a method for detecting the randomness of data sequences through first-order autocorrelation. One-bit sampling is adopted to reduce the complexity of the sampling circuit and data processing algorithm. The effectiveness of the DW test in the situation of one-bit sampling is demonstrated through simulation and analysis. Simulation results show that the proposed technique is capable of detecting weak sinusoidal and square-wave signals with a signal-to-noise ratio (SNR) above -30 dB, and the frequency or SNR of a weak signal can be further estimated based on mutual constraints. The measured results confirm the capability. In addition, the factors of coherent sampling, noise bandwidth, and comparator threshold that influence the performance of the proposed technique are simulated and discussed in detail.

Neural populations within macaque early vestibular pathways are adapted to encode natural self-motion.

How the activities of large neural populations are integrated in the brain to ensure accurate perception and behavior remains a central problem in systems neuroscience. Here, we investigated population coding of naturalistic self-motion by neurons within early vestibular pathways in rhesus macaques (Macacca mulatta). While vestibular neurons displayed similar dynamic tuning to self-motion, inspection of their spike trains revealed significant heterogeneity. Further analysis revealed that, during natural but not artificial stimulation, heterogeneity resulted primarily from variability across neurons as opposed to trial-to-trial variability. Interestingly, vestibular neurons displayed different correlation structures during naturalistic and artificial self-motion. Specifically, while correlations due to the stimulus (i.e., signal correlations) did not differ, correlations between the trial-to-trial variabilities of neural responses (i.e., noise correlations) were instead significantly positive during naturalistic but not artificial stimulation. Using computational modeling, we show that positive noise correlations during naturalistic stimulation benefits information transmission by heterogeneous vestibular neural populations. Taken together, our results provide evidence that neurons within early vestibular pathways are adapted to the statistics of natural self-motion stimuli at the population level. We suggest that similar adaptations will be found in other systems and species.

CORRELATION ANALYSIS OF NORMAL AND JAUNDICE-RELATED BIRTHS: A COMPARATIVE STUDY IN GENERAL HOSPITAL AGBOR (SEMI-URBAN) AND FEDERAL MEDICAL CENTER ASABA (URBAN), 2013-2022

CORRELATION ANALYSIS OF NORMAL AND JAUNDICE-RELATED BIRTHS: A COMPARATIVE STUDY IN GENERAL HOSPITAL AGBOR (SEMI-URBAN) AND FEDERAL MEDICAL CENTER ASABA (URBAN), 2013-2022

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

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

Study on vibration signals identification method for pipeline leakage detection based on deep learning technology

For natural gas pipeline leakage systems based on distributed feedback fiber laser vibration sensors (DFB-FL), a novel neural network model (mCNN-LFLBs) integrating multi-scale convolutional neural networks (mCNN) with local feature extraction blocks (LFLBs) is proposed in this study. To address the problem of feature loss in the traditional signal image transfer process, the one-dimensional original signal is directly used as the input of the model, effectively maintaining the time-domain correlation of the leaked signal. The time-domain correlation of the leakage signal is preserved. To address the problem that traditional pattern recognition algorithms have a weak ability to extract deep features and local features, mCNN is established by this model, which realizes the use of convolution kernels of different scales to extract features of different frequency bands in one-dimensional signals. Additionally, LFLBs are introduced, which enhances the local expression ability of the model, further improves the feature mining quality of the model, and better presents the deep features of leaked signals. Experimental results demonstrate that the mCNN-LFLBs model has a classification accuracy of up to 98.6%, with a testing response time of approximately 10 ms, and outstanding performance in terms of convergence speed. These results highlight the potential practical application of the mCNN-LFLBs model in natural gas leak detection.

On the Regularization of Recursive Least-Squares Adaptive Algorithms Using Line Search Methods

Stereophonic acoustic echo cancellation (SAEC) requires the identification of four unknown impulse responses corresponding to four loudspeaker-to-microphone pairs. Recent developments in the field of adaptive filters for SAEC setups have allowed for the handling of a single complex-valued adaptive impulse response, instead of the four classical real-valued adaptive filters. With the simplified framework provided by the widely linear (WL) model, more advanced versions of recursive least-squares (RLS) were employed in order to take advantage of their superior tracking speeds when working with highly correlated input signals (such as speech). Despite the performances and numerical stability provided by using exponentially weighted versions of the RLS family in combination with line search methods (LSMs), the SAEC configurations have limited capabilities in mitigating the negative effects caused by high-level disturbances affecting the two microphone signals. Such is the case of double-talk scenarios, which considerably reduce the update accuracy of the adaptive system. This paper analyzes a regularization technique for the named WL-RLS-LSM adaptive filters by adjusting the correlation matrix associated with the input signals and creating a reaction in the update process. The proposed method is designed to considerably slow (or even freeze) the adaptation process while the disturbance is manifested. Simulation results are discussed in order to validate the theoretical content.

EEG emotion recognition based on differential entropy feature matrix through 2D-CNN-LSTM network

Emotion recognition research has attracted great interest in various research fields, and electroencephalography (EEG) is considered a promising tool for extracting emotion-related information. However, traditional EEG-based emotion recognition methods ignore the spatial correlation between electrodes. To address this problem, this paper proposes an EEG-based emotion recognition method combining differential entropy feature matrix (DEFM) and 2D-CNN-LSTM. In this work, first, the one-dimensional EEG vector sequence is converted into a two-dimensional grid matrix sequence, which corresponds to the distribution of brain regions of the EEG electrode positions, and can better characterize the spatial correlation between the EEG signals of multiple adjacent electrodes. Then, the EEG signal is divided into equal time windows, and the differential entropy (DE) of each electrode in this time window is calculated, it is combined with a two-dimensional grid matrix and differential entropy to obtain a new data representation that can capture the spatiotemporal correlation of the EEG signal, which is called DEFM. Secondly, we use 2D-CNN-LSTM to accurately identify the emotional categories contained in the EEG signals and finally classify them through the fully connected layer. Experiments are conducted on the widely used DEAP dataset. Experimental results show that the method achieves an average classification accuracy of 91.92% and 92.31% for valence and arousal, respectively. The method performs outstandingly in emotion recognition. This method effectively combines the temporal and spatial correlation of EEG signals, improves the accuracy and robustness of EEG emotion recognition, and has broad application prospects in the field of emotion classification and recognition based on EEG signals.

An integrated waveform design method for underwater acoustic detection and communication

AbstractIn recent years, the integrated system for underwater detection and communication (ISUDC) has become one of the important research directions. At present, the efficiency of the existing shared signals carrying communication information is low, and the synchronization performance needs to be further improved. So, this paper proposes an integrated waveform design scheme for ISUDC based on the carrier with good detection performance and binary phase shift differential keying (DBPSK). In this scheme, the signal with better underwater detection performance (e.g. Linear Frequency Modulation, LFM) is selected as the carrier, and DBPSK is used as the modulation mode to modulate the communication symbols into the carrier. However, the correlation of the detection signal will be destroyed. Thus, the down‐frequency detection signal (e.g. ngLFM) is superimposed into the signal to form a shared signal (LFM‐DBPSK‐ng, LDN). The detection performance, synchronization performance and communication performance of the shared signal are analyzed by ambiguity function, cross‐correlation and BER, respectively. In addition, according to the proposed scheme, the shared signal HFM‐DBPSK‐ng (HDN) is generated by using HFM as carrier. The comparative simulation experiments are carried out with the existing LFM‐BPSK signals. Experiments show that both LDN and HDN have better performance compared to the other shared signals, which proves the effectiveness of the integrated waveform design scheme for ISUDC proposed in this paper.

Similarity to earthquakes again: periodic radio pulses of the magnetar SGR 1935+2154 are accompanied by aftershocks like fast radio bursts

ABSTRACT It was recently discovered that the time correlations of repeating fast radio bursts (FRBs) are similar to earthquake aftershocks. Motivated by the association between FRBs and magnetars, here we report correlation function analyses in the time-energy space for the 563 periodic radio pulses and the 579 X-ray short bursts from the magnetar SGR 1935+2154, which is known to have generated FRBs. Although radio pulses are concentrated near the fixed phase of the rotational cycle, we find that when multiple pulses occur within a single cycle, their correlation properties (aftershock production probability, aftershock rate decaying in power of time, and more) are similar to those of extragalactic FRBs and earthquakes. A possible interpretation is that the radio pulses are produced by rupture of the neutron star crust, and the first pulse within one cycle is triggered by external force periodically exerted on the crust. The periodic external force may be from the interaction of the magnetosphere with material ejected in an outburst. For X-ray bursts, we found no significant correlation signal, though correlation on the same time-scale as radio pulses may be hidden due to the long event duration. The aftershock similarity between the periodic radio pulsation and FRBs is surprising, given that the two are energetically very different, and therefore the energy sources would be different. This suggests that the essence of FRB-like phenomena is starquakes, regardless of the energy source, and it is important to search for FRB-like bursts from neutron stars with various properties or environments.

A state estimation method based on CNN-LSTM for ball screw

Ball screw is widely used in the engineering field, and accurate estimation of their state is crucial for the reliability of system operation. However, existing methods often overlook the time series characteristics and spatial correlation of vibration signals, unable to provide complete degradation information and divide the degradation process, resulting in limited prediction accuracy. Therefore, a state estimation method for ball screw based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Neural Networks (LSTM) is proposed. An experiment of ball screw transmission equipment was conducted to collect vibration signals throughout the entire life cycle and verify the proposed method. Firstly, the frequency domain amplitude signal of the transformed ball screw is normalized to eliminate scale differences, which serves as the input for CNN feature extraction. Then, these deep features are input into the LSTM network to capture the fault evolution patterns that reveal the degradation of ball screw performance, and achieve accurate estimation of ball screw state. The final prediction accuracy was 97.87%, verifying the effectiveness of the proposed method.

Transcriptomic meta-analysis characterizes molecular commonalities between psoriasis and obesity.

Despite the abundance of epidemiological evidence for the high comorbid rate between psoriasis and obesity, systematic approaches to common inflammatory mechanisms have not been adequately explored. We performed a meta-analysis of publicly available RNA-sequencing datasets to unveil putative mechanisms that are postulated to exacerbate both diseases, utilizing both late-stage, disease-specific meta-analyses and consensus gene co-expression network (cWGCNA). Single-gene meta-analyses reported several common inflammatory mechanisms fostered by the perturbed expression profile of inflammatory cells. Assessment of gene overlaps between both diseases revealed significant overlaps between up- (n = 170, P value = 6.07 × 10-65) and down-regulated (n = 49, P value = 7.1 × 10-7) genes, associated with increased T cell response and activated transcription factors. Our cWGCNA approach disentangled 48 consensus modules, associated with either the differentiation of leukocytes or metabolic pathways with similar correlation signals in both diseases. Notably, all our analyses confirmed the association of the perturbed T helper (Th)17 differentiation pathway in both diseases. Our novel findings through whole transcriptomic analyses characterize the inflammatory commonalities between psoriasis and obesity implying the assessment of several expression profiles that could serve as putative comorbid disease progression biomarkers and therapeutic interventions.

Neuromorphic Event Signal-Driven Network for Video De-raining

Convolutional neural networks-based video de-raining methods commonly rely on dense intensity frames captured by CMOS sensors. However, the limited temporal resolution of these sensors hinders the capture of dynamic rainfall information, limiting further improvement in de-raining performance. This study aims to overcome this issue by incorporating the neuromorphic event signal into the video de-raining to enhance the dynamic information perception. Specifically, we first utilize the dynamic information from the event signal as prior knowledge, and integrate it into existing de-raining objectives to better constrain the solution space. We then design an optimization algorithm to solve the objective, and construct a de-raining network with CNNs as the backbone architecture using a modular strategy to mimic the optimization process. To further explore the temporal correlation of the event signal, we incorporate a spiking self-attention module into our network. By leveraging the low latency and high temporal resolution of the event signal, along with the spatial and temporal representation capabilities of convolutional and spiking neural networks, our model captures more accurate dynamic information and significantly improves de-raining performance. For example, our network achieves a 1.24dB improvement on the SynHeavy25 dataset compared to the previous state-of-the-art method, while utilizing only 39% of the parameters.

On the interplay between state-dependent reconfigurations of global signal correlation and BOLD fluctuations: An fMRI study

BackgroundThe dynamics of global, state-dependent reconfigurations in brain connectivity are yet unclear. We aimed at assessing reconfigurations of the global signal correlation coefficient (GSCORR), a measure of the connectivity between each voxel timeseries and the global signal, from resting-state to a stop-signal task. The secondary aim was to assess the relationship between GSCORR and blood-oxygen-level-dependent (BOLD) activations or deactivation across three different trial-conditions (GO, STOP-correct, and STOP-incorrect). MethodsAs primary analysis we computed whole-brain, voxel-wise GSCORR during resting-state (GSCORR-rest) and stop-signal task (GSCORR-task) in 107 healthy subjects aged 21–50, deriving GSCORR-shift as GSCORR-task minus GSCORR-rest. GSCORR-tr and trGSCORR-shift were also computed on the task residual time series to quantify the impact of the task-related activity during the trials. To test the secondary aim, brain regions were firstly divided in one cluster showing significant task-related activation and one showing significant deactivation across the three trial conditions. Then, correlations between GSCORR-rest/task/shift and activation/deactivation in the two clusters were computed. As sensitivity analysis, GSCORR-shift was computed on the same sample after performing a global signal regression and GSCORR-rest/task/shift were correlated with the task performance. ResultsSensory and temporo-parietal regions exhibited a negative GSCORR-shift. Conversely, associative regions (ie. left lingual gyrus, bilateral dorsal posterior cingulate gyrus, cerebellum areas, thalamus, posterolateral parietal cortex) displayed a positive GSCORR-shift (FDR-corrected p < 0.05). GSCORR-shift showed similar patterns to trGSCORR-shift (magnitude increased) and after global signal regression (magnitude decreased). Concerning BOLD changes, Brodmann area 6 and inferior parietal lobule showed activation, while posterior parietal lobule, cuneus, precuneus, middle frontal gyrus showed deactivation (FDR-corrected p < 0.05). No correlations were found between GSCORR-rest/task/shift and beta-coefficients in the activation cluster, although negative correlations were observed between GSCORR-task and GO/STOP-correct deactivation (Pearson rho=-0.299/-0.273; Bonferroni-p < 0.05). Weak associations between GSCORR and task performance were observed (uncorrected p < 0.05). ConclusionGSCORR state-dependent reconfiguration indicates a reallocation of functional resources to associative areas during stop-signal task. GSCORR, activation and deactivation may represent distinct proxies of brain states with specific neurofunctional relevance.

Time transfer via single‐record TDoA measurements of GNSS satellites using direct cross‐correlation and relative pilot code phases

AbstractRadar systems are evolving towards distributed receiver networks. As individual stations might be separated too far to install a cable link, novel methods are required to synchronise individual data records in the time domain. State‐of‐the‐art GNSS receivers disciplining a highly stable oscillator are able to output a timing signal with several nanosecond accuracy solely using non‐proprietary signals. However, they typically require a stable environment and become a major cost factor for receiver networks with a high number of nodes. A method is presented to passively synchronise data records via GNSS raw signals in a single record requiring only a GNSS antenna, an analogue‐to‐digital converter and computation hardware. The clock bias is estimated via the common view method with either full raw signal correlation or software‐based code correlation of individual signals from the GPS, Galileo and BeiDou constellation with sub‐nanosecond precision.

Abstract 4696: ERBB2 signaling and inhibition in bladder cancer

Abstract The role of human epidermal growth factor receptor-2 (HER2) protein and the inhibition of its oncogenic signaling pathway, ERBB2, is well defined and found to be beneficial in combination with standard chemotherapies in breast and certain gastro-intestinal cancers. Targeting the ERBB2 pathway has had unclear or inconsistent results in early trials in bladder cancer (BLCA) and the efficacy of its combination with cisplatin, a standard BLCA chemotherapy, is not known. In this study, we identify the transcriptomic oncogenic signatures that correlate with ERBB2 signaling in BLCA, explore how HER2 expression in BLCA cell lines correlates with treatment response in vitro, and evaluate the potential of ERBB2 inhibition in combination with cisplatin for BLCA. We analyzed the tumor transcriptome from the Cancer Genome Atlas (TCGA) study of BLCA with single sample Gene Set Enrichment Analyses (ssGSEA) for Hallmark and Oncogenic gene sets. We performed multiple Pearson correlation analyses of HER2 protein levels and ERBB2 signaling with other signaling pathways in the TCGA BLCA tumors and adjusted the significance for multiple comparisons using the Benjamini-Hochberg method. Immunoblotting was used to assess HER2 protein levels across a panel of bladder cancer cell lines. We then assessed cell viability and colony formation upon exposure to the specific ERBB2 inhibitor, tucatinib. Synergy between cisplatin and tucatinib in BLCA cell lines was evaluated using the Chou-Talalay method. In the TCGA BLCA dataset, HER2 protein positively correlates with ERBB2 signaling with a Pearson correlation coefficient of 0.45 and an adjusted p-value of 1.73E-17. ERBB2 signaling has multiple significant positive and negative correlations. ERBB2 signaling negatively correlates with the DNA repair gene set with a Pearson coefficient of -0.5 and an adjusted p-value of 1.40E-22. HER2 protein expression varies greatly between bladder cancer cell lines: HT-1197 and UMUC3 have lower HER2 expression, with HT-1376, T24, RT4, 5637, and SW780 showing higher HER2 expression. HT-1197 and RT4 had the lowest IC50s for tucatinib and showed a strong response to low doses of tucatinib in the colony formation assay. Cisplatin and tucatinib have synergetic growth inhibition (combination index &amp;lt;1) at low concentration levels in the HT-1197 and SW780 cell lines. Multiple oncogenic pathways coexist with ERBB2 signaling in BLCA. The varied response to specific ERBB2 inhibition and the lack of correlation of the response with HER2 protein level in cell lines alludes to alternative oncogenic pathways. A strong negative correlation of ERBB2 signaling with the DNA response pathway in the TCGA BLCA dataset, combined with preclinical evidence of synergy between tucatinib and cisplatin, indicates a potential for development of the combination as a clinical treatment strategy and warrants further study. Citation Format: Constance Caparas-Spaugy, Chris Stubben, Ken Boucher, Bingjian Feng, Aaron Atkinson, David Nix, Bradley Cairns, Sumati Gupta. ERBB2 signaling and inhibition in bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4696.

A novel stress concentration inspection method for marine oil and gas pipeline based on UNSM

Stress concentration of marine oil and gas pipelines can lead to deformation and cracks, laying out great hidden dangers for safe operation. This paper proposes a novel in-line inspection method for stress concentration based on UNSM (unsaturated magnetization) for oil and gas pipelines. Firstly, the stress concentration inspection mechanism with UNSM is analyzed, and the equivalent magnetic circuit model of the inspection probe is derived. Secondly, the response signal features of axial, radial and complex stresses were investigated parametrically by numerical simulation. Then, quantitative tensile stress inspection was carried out with the UNSM inspection probe. Finally, the pipeline stress state is continuously scanned by the self-developed defect dynamic scanning experimental system. The results show that the UNSM inspection probe can effectively detect the abnormal stress of the pipeline. Meanwhile, the axial tensile, radial compressive stress and complex stress increase the axial permeability of the pipeline, resulting in a negative correlation of characteristic signals. Besides, the signal feature shows the "basin" distribution and decreases gradually with the increase of the lift-off. The above research provides a reference for developing in-line inspection equipment for the abnormal stress of marine pipelines.

DOA Estimation Approach Based on Parallel Stochastic Resonance System and Signal Correlation Calculation

DOA Estimation Approach Based on Parallel Stochastic Resonance System and Signal Correlation Calculation