Adaptive Signal Research Articles

Targeting a chemo-induced adaptive signaling circuit confers therapeutic vulnerabilities in pancreatic cancer

Advanced pancreatic ductal adenocarcinomas (PDACs) respond poorly to all therapies, including the first-line treatment, chemotherapy, the latest immunotherapies, and KRAS-targeting therapies. Despite an enormous effort to improve therapeutic efficacy in late-stage PDAC patients, effective treatment modalities remain an unmet medical challenge. To change the status quo, we explored the key signaling networks underlying the universally poor response of PDAC to therapy. Here, we report a previously unknown chemo-induced symbiotic signaling circuit that adaptively confers chemoresistance in patients and mice with advanced PDAC. By integrating single-cell transcriptomic data from PDAC mouse models and clinical pathological information from PDAC patients, we identified Yap1 in cancer cells and Cox2 in stromal fibroblasts as two key nodes in this signaling circuit. Co-targeting Yap1 in cancer cells and Cox2 in stroma sensitized PDAC to Gemcitabine treatment and dramatically prolonged survival of mice bearing late-stage PDAC, whereas simultaneously inhibiting Yap1 and Cox2 only in cancer cells was ineffective. Mechanistically, chemotherapy triggers non-canonical Yap1 activation by nemo-like kinase in 14-3-3ζ-overexpressing PDAC cells and increases secretion of CXCL2/5, which bind to CXCR2 on fibroblasts to induce Cox2 and PGE2 expression, which reciprocally facilitate PDAC cell survival. Finally, analyses of PDAC patient data revealed that patients who received Statins, which inhibit Yap1 signaling, and Cox2 inhibitors (including Aspirin) while receiving Gemcitabine displayed markedly prolonged survival compared to others. The robust anti-tumor efficacy of Statins and Aspirin, which co-target the chemo-induced adaptive circuit in the tumor cells and stroma, signifies a unique therapeutic strategy for PDAC.

Coordination of distributed adaptive signal control and advisory speed optimization based on shockwave theory

AbstractThis paper presents a distributed adaptive signal control and advisory speed coordination method based on shockwave theory, which accommodates diverse traffic conditions. In order to assess signal control efficiency under various scenarios, an innovative evaluation index termed synthetic delay is introduced based on the analysis of traffic dynamics at intersections. Considering the formation and dissipation of queue, and flow fluctuation of incoming traffic, it automatically evaluates control delay and throughput with distinctive significances. The distributed adaptive control method calculates the optimal green time in real time to minimize total synthetic delay at intersections. Furthermore, the coordination of advisory speed with the signal control schemes is addressed to ensure smooth progressions for vehicles. The proposed method considers the saturation of traffic and upstream traffic flow changes, leading to adaptability to changing traffic scenarios and effective coordination of traffic control. Several simulations were conducted and compared with the proposed method with other control methods. The results demonstrate that the proposed methods reduce the control delay and increase intersection throughput remarkably under different traffic saturations, confirming their effectiveness.

Displaying Altruism as a Sexual Signal in Human Mate Choice is an Adaptation – An Interdisciplinary Overview of the Evidence

AbstractNumerous studies in humans have shown that altruism may influence mate choice decisions, highlighting its potential role as a sexual signal. Whether using altruism as a signal is an adaptation, however, remains largely unresolved. In this review, we aim to investigate the adaptive nature of altruism as a sexual signal by constructing a comprehensive nomological network of evidence. Drawing upon interdisciplinary scientific modes of evidence (psychology, medicine, physiology, genetics, phylogenetics, hunter-gatherer studies, cross-cultural studies, and theoretical studies), our analysis reveals an extensive breadth of evidence across the various disciplines supporting the hypothesis that altruism is an adaptive sexual signal. However, only the psychological evidence demonstrates substantial depth. Other fields exhibit a lack of variation in research methods or a minimal amount of studies, limiting the strength of their respective contributions. Despite these limitations, there is still sufficient support for the nomological network. Based on the evidence of the various disciplines, we conclude that there is sufficient support that altruism as a sexual signal is an adaptation. We provide considerations for future research that would expand the scope of the evidentiary depth across the various disciplines, which would strengthen our understanding of the role of sexual selection in human altruism.

Unification of probabilistic graph model and deep reinforcement learning (UPGMDRL) for multi-intersection traffic signal control

Traffic signals play a pivotal role in modern life by preventing collisions, regulating traffic flow, and ensuring a predictable and efficient transportation system. Adaptive traffic light signal control (ATSC) is a promising paradigm for mitigating traffic congestion in Intelligent Transportation Systems (ITS). Among various AI-based approaches, Deep Reinforcement Learning (DRL) has gained widespread application, demonstrating superior performance. This paper aims to develop a latent space reinforcement learning method for intelligent traffic control, with a focus on making explainable decisions. According to the latent model and hidden Markov mixed model, this method integrated both to develop an ATSC framework for traffic networks with multiple intersections. Given the challenges posed by high-dimensional data and a limited understanding of the task, traditional decision-making methods often struggle with understanding the environment. This paper aims to provide semantic information and an enhanced understanding of the environment by offering interpretable states. The latent model is employed to extract task-relevant information from underlying representations within a framework that unifies representation learning and DRL. The experimental results demonstrate how our approach effectively and efficiently balances traffic flow, leading to improved traffic management.

Rolling bearing fault diagnosis method based on adaptive signal diagnosis network and its application

Aiming at the problems of fixed convolution kernel size and poor targeting of extracted features in the application of deep learning in fault diagnosis, this paper develops a fault diagnosis framework called adaptive signal diagnosis network (ASDN), in which the EEMD of multi-scale signal decomposition is improved in the adaptive preprocessing stage to adaptively capture the transient changes of signals. Meanwhile, SSA is improved to further extract fault trends and periodic components to optimize the signal representation. In the adaptive deep learning stage, an innovative temporal convolutional network (TCN) with a dynamic adjustment mechanism was developed to enable the neural network to adjust its convolutional kernel size according to different frequency components so as to accurately process signals of different frequencies. Validation on datasets from Case Western Reserve University and Xi'an Jiaotong University shows the superior performance of the proposed method, with diagnostic accuracies of 100% and 97.42% on these two public datasets, respectively.

Household energy consumption forecasting based on adaptive signal decomposition enhanced iTransformer network

With the presence of volatility and noise in the energy consumption data, existing energy consumption forecasting methods have difficulties in achieving satisfactory forecasting results from multiple perspectives, including accuracy, robustness, generalization, and efficiency. This study introduces a novel energy consumption forecasting framework that combines the adaptive signal decomposition method and iTransformer (ASSA-iTransformer) to tackle the above-mentioned difficulties. Each subsequence obtained by ASSA decomposition was fed into the iTransformer framework, and deep-level time series information was extracted. Compared to state-of-the-art methods across various evaluation dimensions, ASSA-iTransformer has improved the average mean absolute error (MAE) by 23.7%, root mean square error (RMSE) by 27.6%, and mean absolute percentage error (MAPE) by 16.4%. Moreover, the R2 values on five real-world household energy consumption datasets are close to 1, demonstrating overall outstanding performance.

An immune signature of postoperative cognitive decline: A prospective cohort study.

Postoperative cognitive decline (POCD) is the predominant complication affecting patients over 60 years old following major surgery, yet its prediction and prevention remain challenging. Understanding the biological processes underlying the pathogenesis of POCD is essential for identifying mechanistic biomarkers to advance diagnostics and therapeutics. This study aimed to provide a comprehensive analysis of immune cell trajectories differentiating patients with and without POCD and to derive a predictive score enabling the identification of high-risk patients during the preoperative period. Twenty-six patients aged 60 years old and older undergoing elective major orthopedic surgery were enrolled in a prospective longitudinal study, and the occurrence of POCD was assessed seven days after surgery. Serial samples collected before surgery, and one, seven, and 90 days after surgery were analyzed using a combined single-cell mass cytometry and plasma proteomic approach. Unsupervised clustering of the high-dimensional mass cytometry data was employed to characterize time-dependent trajectories of all major innate and adaptive immune cell frequencies and signaling responses. Sparse machine learning coupled with data-driven feature selection was applied to the pre-surgery immunological dataset to classify patients at risk for POCD. The analysis identified cell-type and signaling-specific immune trajectories differentiating patients with and without POCD. The most prominent trajectory features revealed early exacerbation of JAK/STAT and dampening of inhibitory κB and nuclear factor-κB immune signaling responses in patients with POCD. Further analyses integrating immunological and clinical data collected before surgery identified a preoperative predictive model comprising one plasma protein and ten immune cell features that classified patients at risk for POCD with excellent accuracy (AUC=0.80, P=2.21e-02 U-test). Immune system-wide monitoring of patients over 60 years old undergoing surgery unveiled a peripheral immune signature of POCD. A predictive model built on immunological data collected before surgery demonstrated greater accuracy in predicting POCD compared to known clinical preoperative risk factors, offering a concise list of biomarker candidates to personalize perioperative management.

Personalized Model-Driven Interventions for Decisions From Experience.

Cognitive models that represent individuals provide many benefits for understanding the full range of human behavior. One way in which individual differences emerge is through differences in knowledge. In dynamic situations, where decisions are made from experience, models built upon a theory of experiential choice (instance-based learning theory; IBLT) can provide accurate predictions of individual human learning and adaptivity to changing environments. Here, we demonstrate how an instance-based learning (IBL) cognitive model, implemented in a cognitive architecture (Adaptive Control of Thought-Rational), can be used to model an individual's decisions in a cybersecurity defense task, accounting for both population average and individual variances. The same IBL model structure with identical architectural parameters generates the full range of human behavior through stochastic memory retrieval processes operating over and contributing to unique experiences. Recurrence quantification analyses allow us to look beyond average behavior between and within individuals to sequential patterns of trial-to-trial behavior. We show how model-tracing and knowledge-tracing techniques can be used to align the model to an individual in real time to drive adaptive and personalized signaling algorithms for a cybersecurity defense system. We also present a method for introspecting into the cognitive model to gain further insight into the cognitive salience of features factored into individual decisions. The combination of techniques provides a blueprint for personalized modeling of individuals. We discuss the results and implications of this adaptive and personalized method for cybersecurity defense and more generally for intelligent artifacts tailored to individual differences in domains such as human-machine teaming.

Adaptive nonlinear filtering algorithms for removal of non-stationary noise in electronystagmographic signals

Non-stationary physiological noise poses significant difficulties due to its time-varying and previously unknown characteristics. When processing electronystagmographic signals, linear filtering can distort diagnostically significant rapid changes caused by saccadic eye movements. In such cases, nonlinear filters based on robust estimators are more appropriate, whereas linear filtering effectively reduces white noise in parts of a signal with linear behaviour. Therefore, an adaptive signal processing approach that varies in nonlinearity from highly nonlinear robust estimation to linear averaging and combines their benefits appears promising. We have proposed a low-complexity adaptive method for switching filter sets and relevant filter parameter settings based on the estimated noise level and local signal behaviour. This method does not require time for filter parameter modification and does not need prior information on the signal model and noise variance. Based on this method, we have designed adaptive nonlinear filtering algorithms to exploit the advantages of both the nonlinear robust and linear averaging estimators. We have evaluated the filtering quality statistically using the minimum mean square error criteria and the maximum signal-to-noise ratio for a model signal with different levels of additive Gaussian noise. The proposed real-time adaptive filtering algorithms demonstrate a significant efficiency improvement compared to the commonly used filters. The proposed adaptive myriad algorithm achieves the highest efficiency by adjusting a sample myriad linearity parameter depending on the local estimate of a signal scale and changing the sliding window length and the coefficient affecting the linearity parameter.

MMD-TSC: An Adaptive Multi-Objective Traffic Signal Control for Energy Saving with Traffic Efficiency

Reducing traffic energy consumption is crucial for smart cities, and vehicle carbon emissions are a key energy indicator. Traffic signal control (TSC) is a useful method because it can affect the energy consumption of vehicles on the road by controlling the stop-and-go of vehicles at traffic intersections. However, setting traffic signals to reduce energy consumption will affect traffic efficiency and this is not in line with traffic management objectives. Current studies adopt multi-objective optimization methods with high traffic efficiency and low carbon emissions to solve this problem. However, most methods use static weights, which cannot adapt to complex and dynamic traffic states, resulting in non-optimal performance. Current energy indicators for urban transportation often fail to consider passenger fairness. This fairness is significant because the purpose of urban transportation is to serve people’s mobility needs not vehicles. Therefore, this paper proposes Multi-objective Adaptive Meta-DQN TSC (MMD-TSC), which introduces a dynamic weight adaptation mechanism to simultaneously optimize traffic efficiency and energy saving, and incorporates the per capita carbon emissions as the energy indicator. Firstly, this paper integrates traffic state data such as vehicle positions, velocities, vehicle types, and the number of passengers and incorporates fairness into the energy indicators, using per capita carbon emissions as the target for reducing energy consumption. Then, it proposes MMD-TSC with dynamic weights between energy consumption and traffic efficiency as reward functions. The MMD-TSC model includes two agents, the TSC agent and the weight agent, which are responsible for traffic signal adjustment and weight calculation, respectively. The weights are calculated by a function of traffic states. Finally, the paper describes the design of the MMD-TSC model learning algorithm and uses a SUMO (Simulation of Urban Mobility) v.1.20.0 for traffic simulation. The results show that in non-highly congested traffic states, the MMD-TSC model has higher traffic efficiency and lower energy consumption compared to static multi-objective TSC models and single-objective TSC models, and can adaptively achieve traffic management objectives. Compared with using vehicle average carbon emissions as the energy consumption indicator, using per capita carbon emissions achieves Pareto improvements in traffic efficiency and energy consumption indicators. The energy utilization efficiency of the MMD-TSC model is improved by 35% compared to the fixed-time TSC.

Genome-wide identification and gene expression networks of LBD transcription factors in Populus trichocarpa

The Lateral Organ Boundaries Domain (LBD) proteins, an exclusive family of transcription factors (TFs) found solely in plants, play pivotal roles in lateral organogenesis, stress adaptation, secondary growth, and hormonal signaling responses. In this study, a total of 55 PtLBD TFs from Populus trichocarpa were identified and systematically classified into two subfamilies, designated as subfamily-I and subfamily-II with seven distinct groups based on phylogenetic analysis. Gene structure detection indicated that the difference of phase numbers linking adjacent exons contribute to the variations in splicing patterns among different PtLBD groups. Numerous transcription factor binding sites and cis-elements pertinent to hormone signaling pathways and stress response mechanisms were identified within the upstream promoter regions of the PtLBD genes. Thirty-five PtLBDs were found to be engaged in either tandem or segmental duplications, and genomic collinearity analysis revealed a stronger alignment between PtLBD genes and eudicots plants compared to their relationship with monocots. GO enrichment and temporal-spatio expression patterns showed that PtLBD7 from subfamily-I and PtLBD20 from subfamily-II, along with other 13 PtLBDs, were involved in plant growth and development biological processes. The multilayered hierarchical gene networks (ML-hGRN) mediated by PtLBD7 and PtLBD20 indicated that PtLBDs were mainly function in poplar growth and stress tolerance through a multifaceted and intricate regulatory machinery. This study lays a solid groundwork for delving deeper into the roles and underlying mechanisms of LBD transcription factors in poplar, specifically those related to plant hormones and stress tolerance.

Enhancing Robustness of Deep Reinforcement Learning Based Adaptive Traffic Signal Controllers in Mixed Traffic Environments Through Data Fusion and Multi-Discrete Actions

Enhancing Robustness of Deep Reinforcement Learning Based Adaptive Traffic Signal Controllers in Mixed Traffic Environments Through Data Fusion and Multi-Discrete Actions

Hidden data recovery using reservoir computing: Adaptive network model and experimental brain signals.

The problem of hidden data recovery is crucial in various scientific and technological fields, particularly in neurophysiology, where experimental data can often be incomplete or corrupted. We investigate the application of reservoir computing (RC) to recover hidden data from both model Kuramoto network system and real neurophysiological signals (EEG). Using an adaptive network of Kuramoto phase oscillators, we generated and analyzed macroscopic signals to understand the efficiency of RC in hidden signal recovery compared to linear regression (LR). Our findings indicate that RC significantly outperforms LR, especially in scenarios with reduced signal information. Furthermore, when applied to real EEG data, RC achieved more accurate signal reconstruction than traditional spline interpolation methods. These results underscore RC's potential for enhancing data recovery in neurophysiological studies, offering a robust solution to improve data integrity and reliability, which is essential for accurate scientific analysis and interpretation.

The mechanism of allosteric activation of SYK kinase derived from multiple phospho-ITAM-bound structures.

Spleen tyrosine kinase (SYK) is central to adaptive and innate immune signaling. It features a regulatory region containing tandem SH2 (tSH2) domains separated by a helical "hinge" segment keeping SYK inactive by associating with the kinase domain. SYK activation is triggered when the tSH2 domains bind to a phosphorylated immunoreceptor tyrosine-based activation motif (ITAM) found on receptor tails. Past mutational studies have indicated that ITAM binding disrupts the hinge-kinase interaction, leading to SYK phosphorylation and activation. However, the mechanism of this process is unclear, as the ITAM interaction occurs far from the hinge region. We have determined crystal structures of three phospho-ITAMs in complex with the tSH2 domains, revealing a highly conserved binding mechanism. These structures, together with mutational studies and biophysical analyses, reveal that phospho-ITAM binding restricts SH2 domain movement and causes allosteric changes in the hinge region. These changes are not compatible with the association of the kinase domain, leading to kinase activation.

Leak aperture recognition of natural gas pipeline based on variational mode decomposition and mutual information

The acoustic emission (AE) signals generated by natural gas pipeline leaks are affected by various noises, making the accurate extraction of feature signals a challenging task. Therefore, this paper introduces a novel adaptive signal denoising approach utilizing Variational Mode Decomposition (VMD).and mutual information (MI). Firstly, the parameters K and α of VMD are optimized using the energy difference. Simultaneously, the correlation coefficient between adjacent IMF components is used as the evaluation criterion to determine the optimal values of K and α. Then, based on MI, a screening criterion is proposed to select effective IMF components for reconstruction. Finally, the ISSA-SVM model was established by introducing Tent chaotic mapping and inertia weight adjustment strategy on the basis of the traditional sparrow search algorithm (SSA). Experimental results demonstrate that the proposed method effectively eliminates noise from pipeline leak AE signals, with a recognition accuracy reaching up to 96.09%.

Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review

The exponential increase in vehicles, quick urbanization, and rising demand for transportation are straining the world’s road infrastructure today. To have a sustainable transportation system with dynamic traffic volume, an Adaptive Traffic Signal Control system (ATSC) should be contemplated to reduce urban traffic congestion and, thus, help reduce the carbon footprints/emissions of greenhouse gases. With dynamic cleave, the ATSC system can adapt the signal timing settings in real-time according to seasonal and short-term variations in traffic demand, enhancing the effectiveness of traffic operations on urban road networks. This paper provides a comprehensive study on the insights, technical lineaments, and status of various research work in ATSC. In this paper, the ATSC is categorized based on several road intersections (RIs), viz., single-intersection (SI) and multiple-intersection (MI) techniques, viz., Fuzzy Logic (FL), Metaheuristic (MH), Dynamic Programming (DP), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and hybrids used for developing Traffic Signal Control (TSC) systems. The findings from this review demonstrate that modern ATSC systems designed using various techniques offer substantial improvements in managing the dynamic density of the traffic flow. There is still a lot of scope to research by increasing the number of RIs while designing the ATSC system to suit real-life applications.

Machine learning solutions for adaptive traffic signal control: A review of image-based approaches

This paper gives an overview and performance evaluation of various machine learning models implemented in management of urban traffic congestions, more specifically, adaptive traffic signal control systems. It considers a review of deep learning algorithms including R- CNN, Fast R-CNN, Faster R-CNN, SSD, YOLO v4, and YOLOv8, with regard to their efficiencies for vehicle detection and traffic prediction under varying scenarios. Certain traffic conditions, camera placements, and environmental factors—related performance for each of the models are discussed. The major performance in most of the scenarios was depicted by the YOLO v4. However, at the same time, YOLOv8 has shown potential to do much better than YOLO v4 on image processing and the resultant accuracy. It also proposes a new algorithm for traffic light timing, whose efficacy is tested using the SUMO simulation platform. While results have shown improvements in urban traffic management, a review underlines that such is in deep need of extensive real-world testing. Future directions should include views from varied angles and weather conditions, and the detection of emergency vehicles, probably with specialized datasets.

A cooperative perception based adaptive signal control under early deployment of connected and automated vehicles

Connected vehicle-based adaptive traffic signal control requires certain market penetration rates (MPRs) to be effective, usually exceeding 10%. Cooperative perception based on connected and automated vehicles (CAVs) can effectively improve overall data collection efficiency and reduce required MPR. However, the distribution of observed vehicles under cooperative perception is highly skewed and imbalanced, especially under very low CAV MPRs (e.g., 1%). To address this challenge, this paper proposes a novel deep reinforcement learning-based adaptive traffic signal control (RL-TSC) method that integrates a traffic flow model, known as the cell transmission model (CTM), denoted as CAVLight. Traffic states estimated from the CTM are integrated with the data collected from the cooperative perception environment to update the states in the CAVLight model. The design of reward function aims for reducing total vehicle delays and stabilizing agent behaviors. Extensive numerical experiments under a real-world intersection with varying traffic demand levels and CAV MPRs are conducted to compare the performance of CAVLight and other benchmark algorithms, including a fixed-time controller, an actuated controller, the max pressure model, and an optimization-based adaptive TSC. Results demonstrate the superiority of CAVLight in performance and generalizability over benchmarks, especially under 1% CAV MPR scenario with high traffic demands. The influence of CTM integration on CAVLight is further explored through RL agent policy visualization and sensitivity analysis in CTM parameters and CAV perception capabilities (i.e., detection range and detection accuracy).

Adaptive signal fusion for swashplate pump fault detection using bidirectional long short-term memory and wavelet scattering transform

In construction equipment, swashplate pumps are vital for uninterrupted operations. However, these pumps often experience multiple simultaneous faults. Current detection methods typically rely on a single signal, insufficient for accurately identifying multiple faults in this hydraulic system. To improve accuracy, we utilize multiple signals that provide comprehensive information, using an adaptive signal-level fusion technique. This method involves analyzing pressure, shaft torque, and swashplate torque signals to identify potential faults. The study employs a Bidirectional Long Short-Term Memory (BiLSTM) model to analyze each type of signal, assessing the model's ability to classify different faults. Once detection accuracy for each fault type is assessed, we propose using a weight matrix to adaptively fuse signals based on their assigned weights. Additionally, the Wavelet Scattering Transform (WST) is utilized for feature extraction from signals with low variance, and Bayesian optimization is applied to find the optimal settings for the BiLSTM model. The proposed approach is tested under various scenarios, including the impact of white Gaussian noise, to evaluate its effectiveness and stability. The results show that the adaptive signal-level fusion approach outperforms traditional methods and individual signal analyses in accurately and robustly detecting swashplate pump faults, highlighting its potential to significantly improve the reliability of construction equipment.

Aging mitochondria in the context of SARS-CoV-2: exploring interactions and implications.

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has presented global challenges with a diverse clinical spectrum, including severe respiratory complications and systemic effects. This review explores the intricate relationship between mitochondrial dysfunction, aging, and obesity in COVID-19. Mitochondria are vital for cellular energy provision and resilience against age-related macromolecule damage accumulation. They manage energy allocation in cells, activating adaptive responses and stress signals such as redox imbalance and innate immunity activation. As organisms age, mitochondrial function diminishes. Aging and obesity, linked to mitochondrial dysfunction, compromise the antiviral response, affecting the release of interferons, and worsening COVID-19 severity. Furthermore, the development of post-acute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID has been associated with altered energy metabolism, and chronic immune dysregulation derived from mitochondrial dysfunction. Understanding the interplay between mitochondria, aging, obesity, and viral infections provides insights into COVID-19 pathogenesis. Targeting mitochondrial health may offer potential therapeutic strategies to mitigate severe outcomes and address long-term consequences in infected individuals.