Pair Of Signals Research Articles

Ameliorated 3 × 3 coupler-based demodulation algorithm using iteratively reweighted ellipse specific fitting.

Passive demodulation scheme using 3 × 3 coupler has been widely used in phase-sensitive optical time-domain reflectometry (φ-OTDR), interrogation of fiber Bragg gratings or fiber optic interferometric sensors, and sensor multiplexing. However, the asymmetry of the 3 × 3 coupler in real applications affects the demodulation performance seriously. We proposed an ameliorated 3 × 3 coupler-based demodulation algorithm using iteratively reweighted ellipse specific fitting (IRESF) to overcome the drawback. IRESF combines iterative reweight technology with ellipse specific fitting, which decreases the weights of high noise points and always outputs ellipse solutions. Any two output signals from the 3 × 3 coupler-based interferometer are fitted by the IRESF and then corrected as a pair of quadrature signals. The stability of the fitting parameters is utilized to resolve the failures of IRESF under small signals. A real-time 1/4 ellipse arc judging module is designed, if the Lissajous figure is larger than 1/4 ellipse arc, IRESF is executed to offer ellipse correction parameters. Otherwise, the fixed parameters preset in the algorithm are used. The fixed parameters are mean values of the fitting parameters of IRESF under a large stimulus. The desired phase signal is finally extracted from the corrected quadrature signals. Experimental results show that the ameliorated algorithm does not require strict symmetry of the 3 × 3 coupler and can work under small signals. The noise floor of the proposed algorithm is -112 dB re rad/√Hz and the demodulated amplitude is 23.15 dB (14.37 rad) at 1 kHz when THD is 0.0488%. Moreover, the response linearity is as high as 99.999%. Compared to the algorithm using direct least squares, the proposed demodulation algorithm is more robust and precise, which has broad application prospects.

Decoding a Cognitive Performance State from Behavioral Data in the Presence of Auditory Stimuli.

Cognitive performance state is an unobserved state that refers to the overall performance of cognitive functions. Deriving an informative observation vector as well as the adaptive model and decoder would be essential in decoding the hidden performance. We decode the performance from behavioral observation data using the Bayesian state-space approach. Forming an observation from the paired binary response with the associated continuous reaction time may lead to an overestimation of the performance, especially when an incorrect response is accompanied by a fast reaction time. We apply the marked point process (MPP) framework such that the performance decoder takes the correct/incorrect responses and the reaction time associated with correct responses as an observation. We compare the MPP-based performance with two other decoders in which the pairs of binary and continuous signals are taken as the observation; one decoder considers an autoregressive (AR) model for the performance state, and the other one employs an autoregressive-autoregressive conditional heteroskedasticity (AR-ARCH) model which incorporates the time-varying and adaptive innovation term within the model. To evaluate decoders, we use the simulated data and the n-back experimental data in the presence of multiple music sessions. The Bayesian state-space approach is a promising way to decode the performance state. With respect to individual perspective, the estimated MPP-based and ARCH-based performance states outperform the AR-based estimation. Based on the aggregated data analysis, the ARCH-based performance decoder outperforms the other decoders. Performance decoders can be employed in educational settings and smart workplaces to monitor one's performance and contribute to developing a feedback controller in closed-loop architecture to improve cognitive performance.

A Deep Learning Approach for Beamforming and Contrast Enhancement of Ultrasound Images in Monostatic Synthetic Aperture Imaging: A Proof-of-Concept.

Goal: In this study, we demonstrate that a deep neural network (DNN) can be trained to reconstruct high-contrast images, resembling those produced by the multistatic Synthetic Aperture (SA) method using a 128-element array, leveraging pre-beamforming radiofrequency (RF) signals acquired through the monostatic SA approach. Methods: A U-net was trained using 27200 pairs of RF signals, simulated considering a monostatic SA architecture, with their corresponding delay-and-sum beamformed target images in a multistatic 128-element SA configuration. The contrast was assessed on 500 simulated test images of anechoic/hyperechoic targets. The DNN's performance in reconstructing experimental images of a phantom and different in vivo scenarios was tested too. Results: The DNN, compared to the simple monostatic SA approach used to acquire pre-beamforming signals, generated better-quality images with higher contrast and reduced noise/artifacts. Conclusions: The obtained results suggest the potential for the development of a single-channel setup, simultaneously providing good-quality images and reducing hardware complexity.

A Potential Mechanism Involved in the Regularity of Center-of-Pressure Displacements During Achieving Unipedal Equilibrium on Stable and Unstable Surfaces.

Sample entropy (SampEn) is a widely used measure for estimating the regularity of center-of-pressure (COP) trajectories, with high COP regularity (low SampEn) commonly interpreted as ineffective postural control. The current study aimed to investigate one possible source of COP regularity-agonist-antagonist coactivation-resulting in joint stiffness or action stability. A cross-correlation analysis was conducted to determine the similarity (coactivation) level of 5 pairs of lower limb agonist-antagonist electromyographic signals. SampEn was applied to anteroposterior (ap) and mediolateral (ml) COP displacements. Electromyographic and COP signals were simultaneously derived from 25 active young adults (25.6 ± 4.0years) during unipedal balancing on stable and multiaxial-unstable surfaces. Then, the correlation between individual agonist-antagonist cross-correlation coefficients and SampEn-COP was explored. The results show that only the tibialis anterior-peroneus longus (TA-PL) coactivations are negatively correlated with SampEn-COPap/ml (P ≤ .030) in both surface conditions, with greater TA-PL coactivation appearing with lower SampEn-COPap/ml. In addition, sex effects as one factor that might influence postural control were also tested. Women exhibited greater TA-PL coactivation (P = .017) and lower SampEn-COPap (P = .005) than men. Together, TA-PL coactivation could be one possible origin of COP regularity, but its effects might be detrimental to unipedal postural control, especially for women.

Stoneley wave detection by acoustic Interferometry: Estimation of shear velocity of a geological formation

Both active and passive full waveform acoustic loggings (FWAL), complemented by a flow log, were conducted in a borehole of an experimental site located in the Cher region (France).. The acoustic tool used for the FWAL experiments is a flexible monopole tool holding a pair of piezoelectric receivers and a magnetostrictive transducer. The tool was modified to perform both active and passive FWAL. For passive acoustic logging, several runs were recorded to obtain a set of acoustic noise sections. As the noise is simultaneously recorded by two receivers of the tool, an interference noise section was elaborated by correlating or deconvolving the pair of signals and then summing these pairs of acoustic traces at each depth. This procedure, which can be interpreted as an interferometry analysis, points out the presence of low-frequency waves identified as Stoneley waves. The velocity and RMS amplitude of the Stoneley wave were computed at each depth. It is shown that: 1- the Stoneley wave velocity obtained in passive mode can be used to estimate the shear velocity of the formation, 2- the RMS amplitude and velocity variations of the Stoneley waves are strongly correlated with the variations of the flowmeter.

Causal gene identification using mitochondria-associated genome-wide mendelian randomization in atrial fibrillation.

Background: Mitochondrial dysfunction is one of the important patho-mechanisms in the development of atrial fibrillation (AF) with underidentified genetic pathophysiology. Methods: Summarized data of methylation, expression and protein abundance levels of mitochondria-related genes were obtained from corresponding studies, respectively. Genes related to mitochondria dysfunction in associations with AF were obtained from the UK Biobank (discovery), and the FinnGen study (replication). Summary-data-based Mendelian randomization analysis (SMR) was performed to assess potential causal relationships between mitochondria-related genes related to the molecular features of AF. Colocalization analysis was further conducted to assess whether the identified signal pairs shared causal genetic variants. Results: Five mitochondria-related genes were found to have causal effects with AF in the sensitivity and the colocalization analyses. Strong associations with increased risk of AF were identified with increased expression level of 4 mitochondria-related genes, including PCCB (OR 1.09, 95% CI 1.05-1.12; PPH4 = 0.95), COX18 (OR 1.83, 95% CI 1.29-2.60; PPH4 = 0.83), SLC25A15 (OR 1.34, 95% CI 1.14-1.58; PPH4 = 0.85), and STX17 (OR 1.16, 95% CI 1.08-1.24; PPH4 = 0.76). Conversely, genetically predicted higher levels expression of UQCC1 (OR 0.94, 95% CI 0.91-0.97) were associated with decreased risk of AF. After further tissue-specific validation, genetically predicted expression levels of PCCB (OR 1.12, 95%, CI 1.01-1.24, p = 0.025) and STX17 (OR 1.13, 95%, CI 1.04-1.23, p = 0.006) in atrial appendage were strongly associated with the increased risk of AF. Conclusion: Mitochondria-related genes are involved either positively (PCCB, COX18, SLC25A15 and STX17) or negatively (UQCCI) in the pathogenesis and the development of AF. These candidate genes may serve as targets for potential development of agents in the prevention and treatment of AF.

A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database.

The opioid epidemic has become a serious national crisis in the United States. An indepth systematic analysis of opioid-related adverse events (AEs) can clarify the risks presented by opioid exposure, as well as the individual risk profiles of specific opioid drugs and the potential relationships among the opioids. In this study, 92 opioids were identified from the list of all Food and Drug Administration (FDA)-approved drugs, annotated by RxNorm and were classified into 13 opioid groups: buprenorphine, codeine, dihydrocodeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, tapentadol, and tramadol. A total of 14,970,399 AE reports were retrieved and downloaded from the FDA Adverse Events Reporting System (FAERS) from 2004, Quarter 1 to 2020, Quarter 3. After data processing, Empirical Bayes Geometric Mean (EBGM) was then applied which identified 3317 pairs of potential risk signals within the 13 opioid groups. Based on these potential safety signals, a comparative analysis was pursued to provide a global overview of opioid-related AEs for all 13 groups of FDA-approved prescription opioids. The top 10 most reported AEs for each opioid class were then presented. Both network analysis and hierarchical clustering analysis were conducted to further explore the relationship between opioids. Results from the network analysis revealed a close association among fentanyl, oxycodone, hydrocodone, and hydromorphone, which shared more than 22 AEs. In addition, much less commonly reported AEs were shared among dihydrocodeine, meperidine, oxymorphone, and tapentadol. On the contrary, the hierarchical clustering analysis further categorized the 13 opioid classes into two groups by comparing the full profiles of presence/absence of AEs. The results of network analysis and hierarchical clustering analysis were not only consistent and cross-validated each other but also provided a better and deeper understanding of the associations and relationships between the 13 opioid groups with respect to their adverse effect profiles.

Multi-omic insight into the molecular networks of mitochondrial dysfunction in the pathogenesis of inflammatory bowel disease

Mitochondrial dysfunction has been linked to the development of inflammatory bowel disease (IBD), but the genetic pathophysiology was not fully elucidated. We employed Mendelian randomization and colocalization analyses to investigate the associations between mitochondrial-related genes and IBD via integrating multi-omics. Summary-level data of mitochondrial gene methylation, expression and protein abundance levels were obtained from corresponding methylation, expression and protein quantitative trait loci studies, respectively. We obtained genetic associations with IBD and its two subtypes from the Inflammatory Bowel Disease Genetics Consortium (discovery), the UK Biobank (replication), and the FinnGen study (replication). We performed summary-data-based Mendelian randomization analysis to assess the associations of mitochondrial gene-related molecular features with IBD. Colocalization analysis was further conducted to assess whether the identified signal pairs shared a causal genetic variant. After integrating the multi-omics data between mQTL-eQTL and eQTL-pQTL, we identified two mitochondrial genes, i.e., PARK7 and ACADM, with tier 1 evidence for their associations with IBD and ulcerative colitis (UC). PDK1 and FISI genes were associated with UC risk with tier 2 and tier 3 evidence, respectively. The methylation of cg05467918 in ACADM was associated with lower expression of ACADM, which fits with the positive effect of cg05467918 methylation on UC risk. Consistently, the inverse associations between gene methylation and gene expression were also observed in PARK7 (cg10385390) and PDK1 (cg17679246), which were corroborated with the protective role in UC. At circulating protein level, genetically predicted higher levels of PARK7 (OR 0.36, 95% CI 0.25-0.52) and HINT1 (OR 0.47, 95% CI 0.30-0.74) were inversely associated with IBD risk; genetically predicted higher level of HINT1 was associated with a decreased risk of Crohn's disease (CD) (OR 0.26, 95% CI 0.14-0.49) and a higher level of ACADM (OR 0.67, 95% CI 0.55-0.83), PDK1 (OR 0.63, 95% CI 0.49-0.81), FIS1 (OR 0.63, 95% CI 0.47-0.83) was associated with a decreased risk of UC. We found that the mitochondrial PARK7 gene was putatively associated with IBD risk, and mitochondrial FIS1, PDK1, and ACADM genes were associated with UC risk with evidence from multi-omics levels. This study identified mitochondrial genes in relation to IBD, which may enhance the understanding of the pathogenic mechanisms of IBD development. XL is supported by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province (LR22H260001) and Healthy Zhejiang One Million People Cohort (K-20230085).

Possible Relation Between Fast Radio Bursts and Gamma Ray Bursts

Fast Radio Bursts (FRBs) have remained an enigmatic cosmic radiation phenomenon with an elusive origin and the question of their potential association with Gamma-Ray Bursts (GRBs) requires further examination. Some of recent studies have indicated a lack of observable data correlation between GRBs and FRBs. This may be attributed to the inadequacy of the currently available datasets, underscoring the necessity for comprehensive data integration from multiple projects. This paper undertakes an analysis of recent hypotheses positing a correlation between FRBs and GRBs. To substantiate these hypotheses, the study compares 680 FRB signal datasets provided by CHIME and FRBCAT with a total of 2975 GRB datasets from Fermi, Swift, and HESS. The objective is to identify correlated FRB-GRB signal pairs. However, it is essential to note that many of the newly collected GRB signals lack crucial measurements, such as afterglow data and redshift values. Consequently, to establish a more robust correlation between specific FRBs and GRBs, additional data collection and subsequent analysis are imperative. In conclusion, this paper summarizes and discusses methods for observing the redshift of subsequent GRBs and presents models for inferring redshift through FRB dispersion. Additionally, it analyzes the temporal distribution of the approximately 109 correlated data points, providing insights into the timing of these cosmic events.

Differences in Functional Connectivity between Patients with Depression with and without Nonsuicidal Self-injury.

: Nonsuicidal self-injury (NSSI), which involves deliberate harm to body tissues without suicidal intent, represents an escalating clinical concern. We used electroencephalography (EEG) to investigate the differences in functional connectivity (FC) patterns in patients with depression with and without a history of NSSI. : Seventy-seven patients with mood disorders experiencing major depressive episodes were categorized into NSSI (Group A; n = 31) and non-NSSI (Group B; n = 46) groups on the basis of their NSSI history. EEG data were collected and FC was analyzed using coherence (Coh), imaginary coherence (iCoh), and phase-locking value (PLV) metrics. Network indices based on graph theory were calculated. Demographic and clinical characteristics and scale scores were compared between groups A and B. : While the two groups showed no significant differences in demographic characteristics such as age and diagnosis, the Beck Depression Inventory and Suicidal Ideation Questionnaire (SIQ) scores were higher in Group A. Binary logistic regression analyses revealed associations of NSSI with sex and the SIQ score. We examined the connectivity of 1,326 pairs of signals across six frequency bands, yielding 7,956 signal pairs. The two groups showed no significant differences in the Coh, iCoh, corrected PLV, or network indices but showed significant differences in all the frequency bands when an uncorrected t test was used. : In this study, FC differences in depression with and without NSSI were not observed. Further well-controlled research is expected to clarify neurobiological underpinnings and guide future interventions.

Terahertz characterization of combined pressure-shear shock loaded aromatic polyurea

Realistic shock loading scenarios imply combined, multiaxial stress states, where the mechanically induced energy competes for failing the material intrinsically due to cohesive failures. Under shock loading, failure encompasses various modes, including spallation, shear bands, microcracking, morphological alteration, phase transformation, and plastic deformation. However, elucidating failure in high strain loading conditions challenges the state-of-the-art characterization due to the disparity interfacing different loading and measurement techniques. This research used a novel spectro-mechanical characterization setup, integrating bulk terahertz spectroscopy synchronized in real-time with mixed-mode laser-induced shock waves. The latter was used to submit polyurea elastomers to concurrent pressure and shear waves by saddling a thin polymer sheet on the hypotenuse of a glass prism. Hence, the laser-induced pressure shock waves underwent simultaneous mode conversion at the glass-polyurea interface at the hypotenuse of the glass prism, effectively submitting the sample to tunable amplitude pressure-shear shock waves. The terahertz spectroscopy, operating in the right-angle reflection configuration, was modified to probe predetermined spots on the polyurea samples at different stress states (adjusted by changing the illumination energy) in a three-step sequence: before, during, and after shock loading. The shock-loaded polyurea samples were also characterized using the optical and scanning electron microscopes, revealing compression-shear and tensile-shear failure modes, including surface depression, nucleation of micro-voids and microcracks, and formation of small planes. The induced stress state based on the illumination energy was quantified using an accompanying finite element simulation, resulting in completing the quasi-isentropic behavior of polyurea elastomers, which was found to be in good agreement with previous experimental data from the plate impact experiments and the predictions based on the Lennard-Jones potential. Time domain analysis of terahertz data, using temporal shifting, hysteresis, and dynamic time warping based on the sum of the Euclidean distances between signal pairs, affirmed the microscopy observations while shedding insights on the elastic and plastic deformations and the reversible conformational changes. Irreversible changes to the macromolecule were also detected using the terahertz data spectral analysis. The current results establish a new experimental framework for fundamentally characterizing polymers and their composites at ultrahigh strain rate loading conditions.

A Novel Method for Quadrature Signal Construction in a Semiconductor Self-Mixing Interferometry System Using a Liquid Crystal Phase Shifter

We have established a novel method for quadrature signal construction in a semiconductor laser diode self-mixing interferometry system using two photodiodes and a beam splitter with a liquid crystal phase shifter (LCPS). This method entails placing an LCPS between the photodiode and the beam splitter so that another phase shift self-mixing signal can be obtained. Then, an arctangent phase algorithm can be used to demodulate the pair of quadrature signals to reconstruct the vibration information of the target object. This method simplifies the self-mixing signal demodulation process and the reconstruction of vibration information. Our experimental results demonstrate the feasibility of using self-mixing phase shifter detection for self-mixing optical measurements. The work illustrates a sort of efficient and referable novel design guidance model which supports the quadrature signals construction in a self-mixing interferometer based on a semiconductor laser diode.

Crosscorrelation-based dynamic time warping and its application in wave equation reflection traveltime inversion

Crosscorrelation and dynamic time warping (DTW) are ubiquitous in time-shift estimation. However, the small-shift limitation of crosscorrelation and the instability and high sensitivity to noise of DTW seriously hinder their applications in complex situations. In this study, we develop a method called crosscorrelation-based DTW (CDTW) to address these issues. Our method constructs error matrices by local crosscorrelation instead of Euclidean distance to minimize the sensitivity to noise. The new error matrices are calculated in local windows and contain local structure similarity information of the two input signals. It improves the stability of the algorithm and makes the CDTW method less sensitive to noise and amplitude modulation. Our method estimates the time-varying shift using dynamic programming as the conventional DTW after the new error matrix is formulated. Numerical tests on pairs of signals and seismic images prove that our method can accurately estimate time shifts in cases of time-varying amplitude modulation and strong random noise contamination. Finally, we apply the CDTW method to wave equation reflection traveltime inversion (WRTI) and develop a CDTW-based WRTI method. Synthetic and field applications prove that this method can construct good background velocity models with the reliable reflection traveltime shifts produced by the CDTW method.

Bivariate retrieval from intensity of cross-correlation

Pulse characterization in ultra-fast optics presents a powerful motivation to study phase retrieval problems of high order. Frequency- and time-resolved techniques for pulse characterization both construct measurements that depend on the intensity of the cross-correlation between two unknown signals undergoing known modulations. The problem of recovering these signals has been traditionally studied and solved with alternating minimization, but recently Wirtinger gradient techniques were demonstrated to invert frequency-resolved measurements on a symmetric signal pair (Pinilla et al., 2019). In this paper, we construct a generalized Wirtinger gradient and Hessian to solve a wide breadth of problems including signal recovery from time- and frequency-resolved measurements. We further demonstrate that both measurement paradigms are special cases of low-rank phase retrieval but with a special structure that disrupts spectral initializers. To combat this problem, we present a tensor-based iterative hard thresholding initializer that, when paired with a Wirtinger gradient descent, is capable of recovering unknown signals with fewer measurements than matrix-based alternating minimization or spectral initialization methods. Finally, we employ Wirtinger gradient descent to recover signals from real-world DSCAN (Wilhelm et al., 2021) measurements and compare results with the existing state-of-the-art.

Robust and scalable distributed recursive least squares

We consider a problem of robust estimation over a network in an errors-in-variables context. Each agent measures noisy samples of a local pair of signals related by a linear regression defined by a common unknown parameter, and the agents must cooperate to find the unknown parameter in presence of uncertainty affecting both the regressor and the regressand variables. We propose a recursive least squares estimation method providing global exponential convergence to the unknown parameter in absence of uncertainty, and robust stability of the estimate, formalized in terms of input-to-state stability, in presence of uncertainty affecting all the variables. The result relies on a cooperative excitation assumption that is proved to be strictly weaker than persistency of excitation of each local data set. The proposed estimator is validated on an adaptive road pricing application.

An Analysis of Satellite Multichannel Differential Code Bias for BeiDou SPP and PPP

Differential code bias (DCB) of satellite is an error that cannot be ignored in precise positioning, timing, ionospheric modeling, satellite clock correction, and ambiguity resolution. The completion of the third generation of BeiDou Navigation Satellite System (BDS-3) has extended DCB to multichannel code bias observations and observable-specific signal bias (OSB). In this paper, the DCB and OSB products provided by the Chinese Academy of Sciences (CAS) are analyzed and compared. The DCB parameters for the BDS satellites are applied in both single- and dual-frequency single point positioning (SPP), and the results are intensively investigated. Based on the satellite DCB parameters of the BDS, the performance of precise point positioning (PPP) with different frequency combinations is also analyzed in terms of positioning accuracy and convergence time. The standard deviations (STDs) of DCBs at each signal pair fluctuate from 0.2 ns to 1.5 ns. The DCBs of BDS-2 are slightly more stable than those of BDS-3. The mean values and STDs of C2I and C7I OSBs for BDS-2 are at the same level and are numerically smaller than their counterparts for the C6I OSBs. The mean OSBs for each signal of the BDS-3, excluding C2I, fluctuate between 12.35 ns and 12.94 ns, and the STD fluctuates between 2.11 ns and 3.10 ns. The DCBs and OSBs of the BDS-3 of the IGSO satellites are more stable than those of the MEO satellites. The corrections for TGD and DCB are able to improve the accuracy of single-frequency SPP by 44.09% and 44.07%, respectively, and improve the accuracy of dual-frequency SPP by 6.44% and 12.85%, respectively. The most significant improvements from DCB correction are seen in single-frequency positioning with B1I and dual-frequency positioning with B2a+B3I. DCB correction can improve the horizontal and three-dimensional positioning accuracy of the dual-frequency PPP of different ionosphere-free combinations by 13.53% and 13.84% on average, respectively, although the convergence is slowed.

The Study of the Bistatic Cross-Correlation Function of Two Signals Separated in Frequency Reflected by the Water Surface

The purpose of this study is to analyze the applicability conditions for the significant wave height (SWH) measurement approach based on measuring the cross-correlation function of two signals with similar frequencies reflected by the sea surface in the bistatic problem statement (the transmitting antenna and the receiving antenna are separated in space). When implementing this approach, the modulus of the normalized cross-correlation function for several pairs of signals with different frequency bases will be measured in the experiment. The advantage of this approach over the traditional method for radar altimetry, based on the analysis of the shape of the reflected pulse, is the high accuracy in measuring the SWH for weak waves. In the bistatic formulation of the problem, an important advantage of the approach under study is the possibility of obtaining analytical formulas for solving the direct problem. This paper presents the derivation of a formula for the modulus of the normalized cross-correlation function of reflected signals, which expresses an explicit relationship with the parameters of sea waves and the measurement geometry in the bistatic formulation of the problem. This paper considers the influence on the modulus of the normalized cross-correlation function of the antenna patterns of the transmitting and receiving antennas, the distances to the sea surface, the wave slope variances, the SWH and the frequency base of the transmitted signals. The optimal variants of the measurement scheme are discussed. The results and conclusions obtained can be easily expanded to underwater acoustic sounding.

Wavelet gated multiformer for groundwater time series forecasting

Developing accurate models for groundwater control is paramount for planning and managing life-sustaining resources (water) from aquifer reservoirs. Significant progress has been made toward designing and employing deep-forecasting models to tackle the challenge of multivariate time-series forecasting. However, most models were initially taught only to optimize natural language processing and computer vision tasks. We propose the Wavelet Gated Multiformer, which combines the strength of a vanilla Transformer with the Wavelet Crossformer that employs inner wavelet cross-correlation blocks. The self-attention mechanism (Transformer) computes the relationship between inner time-series points, while the cross-correlation finds trending periodicity patterns. The multi-headed encoder is channeled through a mixing gate (linear combination) of sub-encoders (Transformer and Wavelet Crossformer) that output trending signatures to the decoder. This process improved the model’s predictive capabilities, reducing Mean Absolute Error by 31.26 % compared to the second-best performing transformer-like models evaluated. We have also used the Multifractal Detrended Cross-Correlation Heatmaps (MF-DCCHM) to extract cyclical trends from pairs of stations across multifractal regimes by denoising the pair of signals with Daubechies wavelets. Our dataset was obtained from a network of eight wells for groundwater monitoring in Brazilian aquifers, six rainfall stations, eleven river flow stations, and three weather stations with atmospheric pressure, temperature, and humidity sensors.

Abstract P1178: APOE-NOTCH Axis Governs Elastogenesis During Human Cardiac Valve Remodeling

Background: Valve remodeling is a complex process involving extracellular matrix organization, development of trilaminar structures, and physical elongation of valve leaflets. However, the cellular and molecular mechanisms regulating valve remodeling and their roles in congenital valve disorders remain poorly understood. Methods: Semilunar valves and atrioventricular valves from healthy and age-matched human fetal hearts with pulmonary stenosis (PS) were collected. Single-Cell RNA-sequencing (scRNA-seq) was performed to determine the transcriptomic landscape of multiple valvular cell subtypes in valve remodeling and disease. Results: scRNA-seq analysis of healthy human fetal valves identified a novel APOE+ elastin-producing valve interstitial cell (Elastin-VIC) subtype spatially located underneath VECs in the direction of unidirectional flow. Knockdown of APOE in fetal VICs resulted in significant elastogenesis defects. In pulmonary valve with PS, we observed decreased expression of APOE and other genes regulating elastogenesis such as EMILIN1 and LOXL1 , as well as elastin fragmentation. Cell-cell interaction analysis revealed that JAG1 from unidirectional VECs activates NOTCH signaling in Elastin-VICs through NOTCH3. In vitro Jag1 treatment in VICs increased the expression of elastogenesis-related genes and enhanced contractile functions with related gene expression. This was accompanied by activation of NOTCH signaling and elastogenesis observed in VICs co-cultured with VECs in the presence of unidirectional flow. Notably, we found that the JAG1-NOTCH3 signaling pair was drastically reduced in the PS valves. Lastly, we demonstrated that APOE is indispensable for JAG1-induced NOTCH activation in VICs, reinforcing the presence of a synergistic intrinsic and external regulatory network involving APOE and NOTCH signaling that is responsible for regulating elastogenesis during human valve remodeling. Conclusion: scRNA-seq analysis of human fetal valves identified a novel Elastin-VIC subpopulation, and revealed mechanism of APOE and NOTCH signaling mediated elastogenesis during valve remodeling, which provided opportunity to understand pathogenesis of elastic malformation within congenital valve disease.

Karyotype and Phylogenetic Relationship Analysis of Five Varieties and Cultivars of Zanthoxylum armatum Based on Oligo-FISH.

Green prickly ash (Zanthoxylum armatum) has edible and medicinal value and is an economically significant plant in many countries. Z. armatum has many cultivars and varieties with similar phenotypes that are difficult to distinguish via traditional methods. In this study, we utilized oligo-FISH to distinguish five varieties and cultivars of Z. armatum on the basis of three oligonucleotide probes of 5S rDNA, (AG3T3)3, and (GAA)6. Karyotype analysis of the five varieties and cultivars of Z. armatum showed that the Z. armatum 'Tengjiao' karyotype formula was 2n = 2x = 98m with karyotype type 1C and an arm ratio of 4.3237, including two pairs of 5S rDNA signals and five pairs of (GAA)6 signals. The karyotype formula of Z. armatum 'Youkangtengjiao' was 2n = 2x = 128m + 8sm with karyotype type 2B and an arm ratio of 3.5336, including three pairs of 5S rDNA signals and 17 pairs of (GAA)6 signals. The karyotype formula of Z. armatum var. novemfolius was 2n = 2x = 134m + 2sm with karyotype type 1C and an arm ratio of 5.5224, including two pairs of 5S rDNA signals and eight pairs of (GAA)6 signals. The karyotype formula of Z. armatum 'YT-03' was 2n = 2x = 2M + 128m + 4sm + 2st with karyotype type 2C and an arm ratio of 4.1829, including three pairs of 5S rDNA signals and nine pairs of (GAA)6 signals. The karyotype formula of Z. armatum 'YT-06' was 2n = 2x = 126m + 10sm with cytotype 2B and an arm ratio of 3.3011, including three pairs of 5S rDNA signals and two pairs of (GAA)6 signals. The five varieties and cultivars of Z. armatum had (AG3T3)3 signals on all chromosomes. The chromosomal symmetry of Z. armatum 'Tengjiao' was high, whereas the chromosomal symmetry of Z. armatum 'YT-03' was low, with the karyotypes of the five materials showing a trend toward polyploid evolution. The phylogenetic relationship between Z. armatum 'Tengjiao' and Z. armatum var. novemfolius was the closest, while that between Z. armatum 'YT-03' and Z. armatum 'YT-06' was closer than with Z. armatum 'Youkangtengjiao' according to oligo-FISH. The results provided a karyotype profile and a physical map that contributes to the distinction of varieties and cultivars of Z. armatum and provides strategies for distinguishing other cultivated species.