Time-varying Changes Research Articles

Changes in Donor-Derived Cell-Free DNA Before and After Rejection and De Novo DSA Detection in Primary and Repeat Kidney Transplant Recipients.

Serial monitoring of dd-cfDNA and change from baseline can provide meaningful information beyond absolute thresholds. We describe dd-cfDNA trajectories from the baseline before and after acute rejection (AR) and de novo donor-specific antibodies (dnDSA) detection in kidney transplant recipients (KTRs). We included KTR from 02/2019 to 03/2022 with serial dd-cfDNA. The primary analysis compared the time-varying change in dd-cfDNA from baseline in KTR first AR on biopsy [AR] to patients with no-AR on biopsy [no-AR]. 151 KTR were analyzed (AR=56 KTR, no-AR=95 KTRs). In the AR group, dd-cfDNA rose ahead of diagnosis: median rise from baseline was 75% at -3 months, 32% at -2 months, and 325% at -1 month before biopsy. At the time of biopsy, the median rise in dd-cfDNA from baseline was 291% (IQR [interquartile range] 88%-1081%) in AR and 17% (IQR 0%- 194%) in no-AR (p<0.0001). Following treatment, dd-cfDNA values fell in the AR group with a median change from baseline of 94.7% at +1 month, 10.5% at +2 months, and 0% at +3 months. These trajectories were not observed in the no-AR group. Similarly, there were no significant differences in eGFR (estimated glomerular filtration rate) trajectories between the two groups. The median change from baseline to dnDSA detection was 141% (IQR 112%-574%). In KTRs with persistent rejection, median dd-cfDNA was 0.95% (IQR 0.44-1.8) compared to 0.19% (IQR 0.12-0.31) in subjects with no rejection on follow-up (p<0.001). The significant changes from baseline observed before and after AR show how serial monitoring enhances dd-cfDNA utility and allows for earlier identification of evolving injury and monitoring treatment response.

Innovative strategies for time-release PCE design and cement paste flowability control

Extending the retention time of cement paste flowability with the addition of admixtures containing slow-release components is standard practice for ensuring the continued building quality of cement-based materials. This study aims to control the release rate of PCE and enhance its time-varying dispersion effect in fresh cement paste by utilizing Time-dependent Release PCEs (TRPCEs) with varying side-chain structures. The structural characterization of the synthesized TRPCEs was investigated using GPC (Gel Permeation Chromatography), FTIR (Fourier Transform Infrared Spectroscopy), and 1H NMR (Proton Nuclear Magnetic Resonance) techniques. Furthermore, experiments were carried out on time-varying flowability with different dosages of TRPCEs to investigate the flowability changes under the time-varying release effect. The influence of TRPCEs on cement hydration was examined using isothermal calorimetry by measuring the early hydration heat of cement paste. The time-dependent adsorption behaviors of TRPCEs were also examined using a TOC (Total Organic Carbon) analyzer. The findings show that TRPCEs have a minor effect on the cement hydration process's rapid reaction period but little effect on its induction and acceleration periods because of variations in the initial dispersion effect. Additionally, the adsorption behaviors of TRPCEs on cement particles vary significantly due to the hydrolysis of slow-release groups. The varying adsorption and consumption of TRPCEs on cement particles and hydration products in different hydration phases are key controlling factors for the time-varying flowability changes in cement paste. Finally, this innovative approach offers a novel perspective for designing and preparing new types of PCEs with a strong capability for time-varying flowability retention.

The time-varying impact of geopolitical risks on financial stress in China: A TVP-VAR analysis

This study employs the TVP-VAR model to investigate the dynamic impact of geopolitical risk (GPR) on China's Financial Stress Index (FSI). The results indicate that GPR is a significant source of financial stress in China. Geopolitical shocks during major geopolitical events or specific periods lead to sharp increases or decreases in China's FSI, demonstrating a complex time-varying impact mechanism. The effects of GPRT and GPRA on FSI show clear time-varying characteristics and structural changes. The impact of GPRC on China's FSI has shifted over time.

306.2: Time-varying relative changes in donor-derived cell-free DNA (dd-cfDNA) during rejection and dnDSA detection in primary and repeat kidney transplant recipients (KTR).

306.2: Time-varying relative changes in donor-derived cell-free DNA (dd-cfDNA) during rejection and dnDSA detection in primary and repeat kidney transplant recipients (KTR).

Time-varying relatedness and structural changes among green growth, clean energy innovation, and carbon market amid exogenous shocks: A quantile VAR approach

Green growth has become a critical driver in sustainability science. However, attaining a steady state of green growth can be challenging in the face of exogenous economic shocks. As such, this study assesses the time-varying relatedness among the USA's green economy (OMXGE), carbon markets (CCAI), green energy (ECO), natural resource markets (SPNGR), and sustainability index (DJS) considering multiple economic traumatism (i.e., the Brexit, COVID-19, Russia-Ukraine conflict). By deploying the quantile VAR (QVAR) and “Wavelet Local Multiple Correlations (WLMC)” approaches, we find: 1) Without the quantile effect, we note strong total interconnectedness among the designated series, which is close to 93 %. 2) From the QVAR findings, we note a high shock-receiving tendency in ECO and CCAI during the Brexit referendum, the first wave of COVID-19, and the Ukraine-Russia conflict, making these markets attractive for further investment during turbulent times. 3) DJS and OMXGE are temporary shock receivers and predominant shock transmitters, indicating that the green economy and sustainability indicators are not robust enough to absorb shocks during economic shocks. 4) Wavelet coherence findings further validate that the green economy is not a safe haven asset, whereas the carbon market has a strong shock absorption capacity and can provide strong hedging support. The underlying findings reveal that Washington's policy toward a renewable-based equity market should be more liberal to ramp up investment in renewables.

Analyzing the online word of mouth dynamics: A novel approach

In today's digital economy, virtually everything from products and services to political debates and cultural phenomena can spark WOM on social media. Analyzing online WOM poses at least three challenges. First, online WOM typically consists of unstructured data that can transform into myriad variables, necessitating effective dimension reduction. Second, online WOM is often continuous and dynamic, with the potential for rapid, time-varying changes. Third, significant events may trigger symmetric or asymmetric responses across various entities, resulting in “bursty” and intense WOM from multiple sources. To address these challenges, we introduce a new computationally efficient method—multi-view sequential canonical covariance analysis. This method is designed to solve the myriad online WOM conversational dimensions, detect online WOM dynamic trends, and examine the shared online WOM across different entities. This approach not only enhances the capability to swiftly interpret and respond to online WOM data but also shows potential to significantly improve decision-making processes across various contexts. We illustrate the method's benefits through two empirical examples, demonstrating its potential to provide profound insights into online WOM dynamics and its extensive applicability in both academic research and practical scenarios.

Neural network enhanced time-varying parameter estimation via weak measurement.

Weak measurement is employed to measure faint signals due to its capability to amplify detection results above technical noise. However, achieving high amplification effects requires accurate adjustment to the experimental system. Estimating unknown time-varying phases, accurately estimating phases, and sensitively perceiving phase changes pose challenges, demanding the system to continuously remain at the appropriate working range. To address this issue, we propose a neural network-based adaptive weak measurement scheme via single-channel light intensity detection. Through machine learning calibrating the experimental system, the reference phase can be dynamically and accurately adjusted, accommodating time-varying phase changes and ensuring the system operates optimally. Compared with traditional dual-channel weak measurement systems, the scheme reduces experimental complexity. Meanwhile, by accurately adjusting the reference phase, the scheme has higher sensitivity and estimation precision compared to the non-modulated scheme. We validate the effectiveness of the scheme in estimating the period and stochastic time-varying phase. The proposed method highlights the advancement of machine learning in weak measurement systems and can also be applied to other quantum-enhanced measurement schemes.

A simplified method for estimating bridge frequency effects considering train mass

The dynamic response of a railway bridge depends on several parameters; the primary parameter is the fundamental natural frequency of vibration of the bridge itself. It is considered a critical parameter of the bridge as the driving or the forcing frequencies arising from moving trains may coincide with the fundamental frequency of the bridge and initiate a resonant response amplifying the bridge load effects. This condition may adversely affect the stresses experienced on bridge members and, consequently, the remaining fatigue life of the structure. Because the train adds additional time-varying mass to the bridge, this introduces a time-varying change in the bridge’s fundamental natural frequency of vibration. As a result, train critical speeds will have a certain range depending on the train configuration. This article presents a simplified method using a power-law relationship to predict the frequency characteristics of a bridge as a function of the train-to-bridge mass ratio. The method is presented in a generalized form, which enables the frequency characteristics to be determined for any given combination of trains and simply supported bridges of short to medium span typically found on the UK rail network. The method is then demonstrated in a case study of a single-span, simply supported plate girder bridge. By considering the BS-5400 train traffic types, the proposed method is used to calculate bridge frequency effects, dynamic amplification, and train critical speed bandwidth for each train type. The simplicity of the proposed method, as it does not require any complex computational modeling, makes it an ideal and effective tool for the practicing engineer to carry out a quick and economical assessment of a bridge for any given train configuration.

Cognitive abilities are associated with rapid dynamics of electrophysiological connectome states

Abstract Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, hold significant implications for cognition. However, connectome dynamics at fast (&amp;gt; 1Hz) timescales highly relevant to cognition are poorly understood due to the dominance of inherently slow fMRI in connectome studies. Here, we investigated the behavioral significance of rapid electrophysiological connectome dynamics using source-localized EEG connectomes during resting-state (N=926, 473 females). We focused on dynamic connectome features pertinent to individual differences, specifically those with established heritability: Fractional Occupancy (i.e., the overall duration spent in each recurrent connectome state) in beta and gamma bands, and Transition Probability (i.e., the frequency of state switches) in theta, alpha, beta, and gamma bands. Canonical correlation analysis found a significant relationship between the heritable phenotypes of sub-second connectome dynamics and cognition. Specifically, principal components of Transition Probabilities in alpha (followed by theta and gamma bands) and a cognitive factor representing visuospatial processing (followed by verbal and auditory working memory) most notably contributed to the relationship. We conclude that rapid connectome state transitions shape individuals' cognitive abilities and traits. Such sub-second connectome dynamics may inform about behavioral function and dysfunction and serve as endophenotypes for cognitive abilities.

Monitoring Time-Varying Changes of Historic Structures Through Photogrammetry-Driven Digital Twinning

Abstract. Historic structures are important for our society but could be prone to structural deterioration due to long service durations and natural impacts. Monitoring the deterioration of historic structures becomes essential for stakeholders to take appropriate interventions. Existing work in the literature primarily focuses on assessing the structural damage at a given moment instead of evaluating the development of deterioration over time. To address this gap, we proposed a novel five-component digital twin framework to monitor time-varying changes in historic structures. A testbed of a casemate in Fort Soledad on the island of Guam was selected to validate our framework. Using this testbed, key implementation steps in our digital twin framework were performed. The findings from this study confirm that our digital twin framework can effectively monitor deterioration over time, which is an urgent need in the cultural heritage preservation community.

Rapid dynamics of electrophysiological connectome states are heritable

Abstract Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infra-slow (&amp;lt;0.1 Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting-state (N = 928, 473 females), we quantified heritability of multivariate (multi-state) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every ∼60–500 ms. Temporal features were heritable, particularly, Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for heritability of dynamic spatial features, specifically states’ Modularity and connectivity pattern. We conclude that genetic effects shape individuals’ connectome dynamics at rapid timescales, specifically states’ overall occurrence and sequencing.

Singular spectrum analysis for the time-variable seasonal signals from GPS in Yunnan Province

Studying the seasonal deformation in GPS time series is important to interpreting geophysical contributors and identifying unmodeled and mismodeled seasonal signals. Traditional seasonal signal extraction used the least squares method, which models seasonal deformation as a constant seasonal amplitude and phase. However, the seasonal variations are not constant from year to year, and the seasonal amplitude and phase are time-variable. In order to obtain the time-variable seasonal signal in the GPS station coordinate time series, singular spectrum analysis (SSA) is conducted in this study. We firstly applied the SSA on simulated seasonal signals with different frequencies 1.00 cycle per year (cpy), 1.04 cpy and with time-variable amplitude are superimposed. It was found that SSA can successfully obtain the seasonal variations with different frequencies and with time-variable amplitude superimposed. Then, SSA is carried out on the GPS observations in Yunnan Province. The results show that the time-variable amplitude seasonal signals are ubiquitous in Yunnan Province, and the time-variable amplitude change in 2019 in the region is extracted, which is further explained by the soil moisture mass loading and atmospheric pressure loading. After removing the two loading effects, the SSA obtained modulated seasonal signals which contain the obvious seasonal variations at frequency of 1.046 cpy, it is close with the GPS draconitic year, 1.040 cpy. Hence, the time-variable amplitude changes in 2019 and the seasonal GPS draconitic year in the region could be discriminated successfully by SSA in Yunnan Province.

Tracking Modal Parameters of Structures Online Using Recursive Stochastic Subspace Identification under Ambient Excitations

Continuous and autonomous system identification is an alternative to regular inspection during operations, which is essential for structural integrity management (SIM) as well as structural health monitoring (SHM). In this regard, online (or real-time) system identification techniques that have recently received considerable attention can be used to assess the current condition and performance during operations and, in the meantime, can be utilized to detect any damage or deterioration. For example, stochastic subspace identification (SSI), based on recursive formulation, has proven its capability in tracking modal parameters as well as time-variant dynamic behaviors. This study proposes the implementation of recursive SSI (RSSI) using the matrix inversion lemma to track slow time-varying parameter changes under ambient excitations. Subsequently, some investigations for practical implementation are examined and discussed. For verifying the reliability of SHM applications based on the proposed methods, two datasets measured from different experiments are exploited to identify the modal parameters reclusively. The results from both numerical simulations and experimental investigations demonstrated the effectiveness of tracking the modal parameters exhibiting time-varying dynamic characteristics under white noise excitations (or ambient excitations).

Multisensory Integration Underlies the Distinct Representation of Odor-Taste Mixtures in the Gustatory Cortex of Behaving Rats.

The perception of food relies on the integration of olfactory and gustatory signals originating from the mouth. This multisensory process generates robust associations between odors and tastes, significantly influencing the perceptual judgment of flavors. However, the specific neural substrates underlying this integrative process remain unclear. Previous electrophysiological studies identified the gustatory cortex as a site of convergent olfactory and gustatory signals, but whether neurons represent multimodal odor-taste mixtures as distinct from their unimodal odor and taste components is unknown. To investigate this, we recorded single-unit activity in the gustatory cortex of behaving female rats during the intraoral delivery of individual odors, individual tastes, and odor-taste mixtures. Our results demonstrate that chemoselective neurons in the gustatory cortex are broadly responsive to intraoral chemosensory stimuli, exhibiting time-varying multiphasic changes in activity. In a subset of these chemoselective neurons, odor-taste mixtures elicit nonlinear cross-modal responses that distinguish them from their olfactory and gustatory components. These findings provide novel insights into multimodal chemosensory processing by the gustatory cortex, highlighting the distinct representation of unimodal and multimodal intraoral chemosensory signals. Overall, our findings suggest that olfactory and gustatory signals interact nonlinearly in the gustatory cortex to enhance the identity coding of both unimodal and multimodal chemosensory stimuli.

Abstract 2210: Healthy dietary patterns and survival among men with prostate cancer

Abstract Background: While prostate cancer is the second leading cause of cancer death in U.S. males, most men with prostate cancer die from other causes. We prospectively examined the potential benefits of adhering to established healthy dietary patterns on overall and cause-specific mortality among men living with prostate cancer. Methods: We studied men with incident prostate cancer in the Health Professionals Follow-up Study from 1986-2022. Five dietary indices-Alternative Healthy Eating Index (AHEI), the Mediterranean diet, Dietary Approaches to Stop Hypertension (DASH), anti-insulinemic, and anti-inflammatory diet scores-were derived from validated food frequency questionnaires administered every 4 years. Post-diagnosis scores were cumulatively averaged, and time-varying changes in scores from pre- to post-diagnosis were calculated. We used multivariable-adjusted Cox models to estimate hazard ratios (HRs, 95% CI) for associations between adherence to dietary patterns with overall and cause-specific survival adjusting for demographics, dietary and lifestyle factors. Results: Among 6,086 prostate cancer patients with 71,783 person-years of follow-up, 3,406 deaths occurred: 578 from prostate cancer, 918 from cardiovascular disease (CVD), and 1910 from other causes. When comparing extreme quintiles of post-diagnosis diet scores, a healthy diet was associated with lower overall mortality for AHEI (0.76, 0.66-0.88) and the Mediterranean diet (0.84, 0.73-0.97). Moreover, comparing extreme quintiles of score change, patients who increased their adherence to these diets after diagnosis had better overall survival with HRs (95% CI) that ranged from 0.79 (0.70-0.89) for AHEI to 0.89 (0.79- &amp;lt;1.00) for the anti-inflammatory diet. No significant associations were observed for DASH. Regarding cause-specific survival, increased adherence to anti-insulinemic diet was associated with better CVD-specific survival (0.78, 0.61-0.99). No significant associations were found between these diet scores and prostate cancer survival. Stronger associations of post-diagnosis diet scores with overall survival were seen in men diagnosed before age 70, those classified as obese at diagnosis (BMI≥30 kg/m2), consuming less alcohol (&amp;lt;7.8 g/d), and with lower grade (Gleason Score ≤7) or stage (T1/T2) tumors. Conclusions: For men diagnosed with prostate cancer, adhering to a healthy dietary pattern after cancer diagnosis, such as the AHEI and Mediterranean Diet, was associated with better overall survival. Increasing adherence to these diets after cancer diagnosis was also associated with improved overall and CVD-specific survival. Citation Format: Binkai Liu, Jane B. Vaselkiv, Caroline Himbert, Colleen B. McGrath, LeeAnn Lucas, Edward Giovannucci, Meir J. Stampfer, Konrad H. Stopsack, Qi Sun, Lorelei A. Mucci. Healthy dietary patterns and survival among men with prostate 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 2210.

Borrower-based measures, house prices and household debt

This paper quantifies the credit-driven housing demand and the role of macro-prudential Loan-to-Income (LTI) and Loan-to-Value (LTV) limits. Using granular and time-varying changes in borrowing capacity, I estimate how shocks in credit availability feed into credit demand and affect household debt. The findings indicate a robust relationship between debt and borrowing capacity that amplifies throughout a housing boom. The relationship is heterogeneous, as changes in borrowing capacity have larger effects among low-income and first-time buyers and in expensive property markets. If no downpayment is required, tightening the LTV limit may not contain borrowing capacity but still curbs leverage among highly-indebted borrowers.

Aberrant dynamic functional connectivity of thalamocortical circuitry in major depressive disorder.

Thalamocortical circuitry has a substantial impact on emotion and cognition. Previous studies have demonstrated alterations in thalamocortical functional connectivity (FC), characterized by region-dependent hypo- or hyper-connectivity, among individuals with major depressive disorder (MDD). However, the dynamical reconfiguration of the thalamocortical system over time and potential abnormalities in dynamic thalamocortical connectivity associated with MDD remain unclear. Hence, we analyzed dynamic FC (dFC) between ten thalamic subregions and seven cortical subnetworks from resting-state functional magnetic resonance images of 48 patients with MDD and 57 healthy controls (HCs) to investigate time-varying changes in thalamocortical FC in patients with MDD. Moreover, dynamic laterality analysis was conducted to examine the changes in functional lateralization of the thalamocortical system over time. Correlations between the dynamic measures of thalamocortical FC and clinical assessment were also calculated. We identified four dynamic states of thalamocortical circuitry wherein patients with MDD exhibited decreased fractional time and reduced transitions within a negative connectivity state that showed strong correlations with primary cortical networks, compared with the HCs. In addition, MDD patients also exhibited increased fluctuations in functional laterality in the thalamocortical system across the scan duration. The thalamo-subnetwork analysis unveiled abnormal dFC variability involving higher-order cortical networks in the MDD cohort. Significant correlations were found between increased dFC variability with dorsal attention and default mode networks and the severity of symptoms. Our study comprehensively investigated the pattern of alteration of the thalamocortical dFC in MDD patients. The heterogeneous alterations of dFC between the thalamus and both primary and higher-order cortical networks may help characterize the deficits of sensory and cognitive processing in MDD.

Bernstein-based estimation of the cross ratio function

Local association measures provide useful insights in time-varying changes in association, especially between time-to-event variables. Such local dependence between two correlated random variables can be measured using the cross ratio function. The cross ratio function is defined as the ratio of conditional hazard functions which have been estimated using Bernstein polynomials before. Alternatively, the cross ratio function can be expressed in terms of (derivatives of) the joint survival function of the two random variables. In this paper, we discuss an alternative Bernstein-based plug-in estimator of the cross ratio function in which each of the ingredients is estimated separately. Next to asymptotic normality of the nonparametric estimator, a simulation study is used to assess its finite-sample performance. Finally, the novel estimator is applied to a real-life data application.

Robust changes in global subtropical circulation under greenhouse warming

The lower tropospheric subtropical circulation (SC) is characterized by monsoons and subtropical highs, playing an important role in global teleconnections and climate variability. The SC changes in a warmer climate are influenced by complex and region-specific mechanisms, resulting in uneven projections worldwide. Here, we present a method to quantify the overall intensity change in global SC, revealing a robust weakening across CMIP6 models. The weakening is primarily caused by global-mean surface warming, and partly counteracted by the direct CO2 effect. The direct CO2 effect is apparent in the transient response but is eventually dominated by the surface warming effect in a slow response. The distinct response timescales to global-mean warming and direct CO2 radiative forcing can well explain the time-varying SC changes in other CO2 emission scenarios. The declined SC implies a contracted monsoon range and drying at its boundary with arid regions under CO2-induced global warming.

Physics-Informed Online Learning by Moving Horizon Estimation: Learning Recurrent Neural Networks in Gray-box Models*

In Model Predictive Control (MPC) closed-loop performance heavily depends on the quality of the underlying prediction model, where such a model must be accurate and yet simple. A key feature in modern MPC applications is the potential for online model adaptation to cope with time-varying changes, part-to-part variations, and complex features of the system dynamics not caught by models derived from first principles. In this paper, we propose to use a physics-informed, or gray-box, model that extends the physics-based model with a data-driven component, namely a Recurrent Neural Network (RNN). Relying on physics-informed models allows for a rather limited size of the RNN, thereby enhancing online applicability compared to pure black-box models. This work presents a method based on Moving Horizon Estimation (MHE) for simultaneous state estimation and learning of the RNN sub-model, a potentially challenging issue due to limited information available in noisy input-output data and lack of knowledge of the internal state of the RNN. We provide a case study on a quadruple tank benchmark showing how the method can cope with part-to-part variations.