Dynamic Signature Research Articles

Immunological characteristics of immunogenic cell death genes and malignant progression driving roles of TLR4 in anaplastic thyroid carcinoma

Anaplastic thyroid carcinoma (ATC) was a rare malignancy featured with the weak immunotherapeutic response. So far, disorders of immunogenic cell death genes (ICDGs) were identified as the driving factors in cancer progression, while their roles in ATC remained poorly clear. Datasets analysis identified that most ICDGs were high expressed in ATC, while DE-ICDGs were located in module c1_112, which was mainly enriched in Toll-like receptor signalings. Subsequently, the ICD score was established to classify ATC samples into the high and low ICD score groups, and function analysis indicated that high ICD score was associated with the immune characteristics. The high ICD score group had higher proportions of specific immune and stromal cells, as well as increased expression of immune checkpoints. Additionally, TLR4, ENTPD1, LY96, CASP1 and PDIA3 were identified as the dynamic signature in the malignant progression of ATC. Notably, TLR4 was significantly upregulated in ATC tissues, associated with poor prognosis. Silence of TLR4 inhibited the proliferation, metastasis and clone formation of ATC cells. Eventually, silence of TLR4 synergistically enhanced paclitaxel-induced proliferation inhibition, apoptosis, CALR exposure and release of ATP. Our findings highlighted that the aberrant expression of TLR4 drove the malignant progression of ATC, which contributed to our understanding of the roles of ICDGs in ATC.

Lattice-based dynamic universal accumulator: Design and application

Dynamic universal accumulator is a crucial cryptography primitive. This type of accumulator can provide a succinct witness for a member (resp. nonmember) in order to make sure whether such member (resp. nonmember) is accumulated in a set, and supports deleting and adding operations from the accumulated set, as well as updating the existing witnesses. In recent years, lattice-based accumulator has given rise to increasing attention with the advent of quantum computing. However, none of existing lattice-based accumulators is both universal and dynamic. Therefore, in this work, we first design a lattice-based dynamic universal accumulator scheme with undeniability and indistinguishability. And we construct a Stern-like zero-knowledge argument protocol about a fresh relation to show that this accumulator supports set membership proofs. Moreover, we use this accumulator and the protocol to construct a full dynamic group signature with stateless updates from lattices, holding traceability and full anonymity. Furthermore, we give an implementation of our accumulator scheme, and the experiment results show that the update algorithms in this scheme have good practicability.

Dynamical signatures of Chaos to integrability crossover in generalized random matrix ensembles

We introduce a two-parameter ensemble of generalized real symmetric random matrices called the β-Rosenzweig–Porter ensemble (β-RPE), parameterized by β, a fictitious inverse temperature of the analogous Coulomb gas model, and γ, controlling the relative strength of disorder. β-RPE encompasses RPE from all of the Dyson’s threefold symmetry classes: orthogonal, unitary and symplectic for . Firstly, we study the energy correlations by calculating the density and 2nd moment of the nearest neighbor spacing (NNS) and robustly quantify the crossover among various degrees of level repulsions. Secondly, the dynamical properties are determined from an exact calculation of the temporal evolution of the fidelity enabling an identification of the characteristic Thouless and the equilibration timescales. The relative depth of the correlation hole in the average fidelity serves as a dynamical signature of the crossover from chaos to integrability and enables us to construct the phase diagram of β-RPE in the γ-β plane. Our results are in qualitative agreement with numerically computed fidelity for matrix ensembles. Furthermore, we observe that for large N the 2nd moment of NNS and the relative depth of the correlation hole exhibit a second order phase transition at γ = 2.

Positive feedbacks in deep-time transitions of human populations.

Abrupt and rapid changes in human societies are among the most exciting population phenomena. Human populations tend to show rapid expansions from low to high population density along with increased social complexity in just a few generations. Such demographic transitions appear as a remarkable feature of Homo sapiens population dynamics, most likely fuelled by the ability to accumulate cultural/technological innovations that actively modify their environment. We are especially interested in establishing if the demographic transitions of pre-historic populations show the same dynamic signature of the Industrial Revolution transition (a positive relationship between population growth rates and size). Our results show that population growth patterns across different pre-historic societies were similar to those observed during the Industrial Revolution in developed western societies. These features, which appear to have been operating during most of our recent demographic history from hunter-gatherers to modern industrial societies, imply that the dynamics of cooperation underlay sudden population transitions in human societies. This article is part of the theme issue 'Evolution and sustainability: gathering the strands for an Anthropocene synthesis'.

Abstract 15639: An Explainable Machine Learning-Based Phenomapping Strategy for Adaptive Predictive Enrichment in Randomized Controlled Trials of Preventive Interventions

Introduction: Randomized controlled trials (RCT) are the cornerstone of evidence-based preventive cardiovascular care but are resource intensive. We propose a novel machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT efficiency. Methods: In simulated group sequential analyses of two cardiovascular outcomes RCTs of (1) a drug (pioglitazone vs placebo; IRIS), and (2) a management strategy (intensive vs standard blood pressure reduction; SPRINT), we constructed dynamic phenomaps to infer patient profiles benefiting from the intervention vs control during interim analyses. We examined their ability to adaptively enrich subsequent trial enrollment. We compared the final study size and effect estimates between the adaptive and original study designs. Results: Across 3 interim analyses, our strategy learned dynamic signatures of individualized benefit. By conditioning prospective participant enrollment on dynamic estimates of personalized benefit, our approach enabled a robust reduction in the trial size across simulations (-18% ± 4.7% in IRIS, p=0.008 [ A ] and -27.4% ± 3.4% in SPRINT, p=0.002 [ B ]), also preserving the original average treatment effect (IRIS: hazard ratio of 0.71 ± 0.01 for pioglitazone vs placebo; vs 0.76 in the original trial [ C ]; SPRINT: hazard ratio of 0.72 ± 0.01 for intensive vs standard systolic blood pressure; vs 0.75 in the original trial; all p<0.01 [ D ]). Hierarchical assessment of safety endpoints revealed no safety concerns with adaptive enrichment, whereas post-hoc review of the enriched populations confirmed that no specific demographic groups were excluded from the analysis. Conclusions: We present an explainable ML algorithm for adaptive predictive enrichment in RCTs leveraging phenome-wide computational measures of personalized benefit. These findings propose a new paradigm to maximize clinical trial efficiency through dynamic data-driven inference.

Primordial stochastic gravitational wave backgrounds from a sharp feature in three-field inflation. Part I. The radiation era

The detection of a primordial stochastic gravitational wave background has the potential to reveal unprecedented insights into the early universe, and possibly into the dynamics of inflation. Generically, UV-complete inflationary models predict an abundance of light scalars, so any inflationary stochastic background may well be formed in a model with several interacting degrees of freedom. The stochastic backgrounds possible from two-field inflation have been well-studied in the literature, but it is unclear how similar they are to the possibilities from many-field inflation. In this work we study stochastic backgrounds from more-than-two field inflation for the first time, focusing on the scalar-induced background produced during the radiation era by a brief turn in three-field space. We find an analytic expression for the enhancement in the power spectrum as a function of the turn rate and the torsion, and show that unique signatures of three-field dynamics are possible in the primordial power spectrum and gravitational wave spectrum. We confirm our analytic results with a suite of numerical simulations and find good agreement in the shape and amplitude of the power spectra. We also comment on the detection prospects in LISA and other future detectors. We do not expect the moderately large growth of the inflationary perturbations necessary for detection to cause a breakdown of perturbation theory, but this must be verified on a case-by-case basis for specific microphysical models to make a definitive claim.

Differential Structural Dynamics Exist between Wild-type and Oncogenic K-Ras.

Distinct dynamic signatures exist between wild-type, G12D mutant, and G12C mutant K-Ras-GTP and K-Ras-GDP.

The dynamical signature and scaling of supramolecular assemblies

We present a brief overview of current investigations on the dynamics of supramolecular assemblies, with the focus on applying broadband dielectric spectroscopy (BDS) combined with different techniques. The dielectric methods have significant advantages in probing the dynamical signature and scaling of supra-structures. We summarize various mechanisms describing supramolecular dynamics, which could produce a relaxation governed by supramolecular association slower than the glass-transition-related structural relaxation. Next, we also discuss the relaxation dynamics in phase-separated supramolecular assemblies and supramolecular assembly under nanoconfinement, for controlling bonus macroscopic performances. This perspective emphasizes the idea that the relaxational response of supramolecular assemblies is generic to some extent. It does not necessarily depend on the chemistry of associations, but could reflect supra-materials’ behaviors determined by their molecular architectures.

Comparative Analysis of Conformational Dynamics and Systematic Characterization of Cryptic Pockets in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 Spike Complexes with the ACE2 Host Receptor: Confluence of Binding and Structural Plasticity in Mediating Networks of Conserved Allosteric Sites.

In the current study, we explore coarse-grained simulations and atomistic molecular dynamics together with binding energetics scanning and cryptic pocket detection in a comparative examination of conformational landscapes and systematic characterization of allosteric binding sites in the SARS-CoV-2 Omicron BA.2, BA.2.75 and XBB.1 spike full-length trimer complexes with the host receptor ACE2. Microsecond simulations, Markov state models and mutational scanning of binding energies of the SARS-CoV-2 BA.2 and BA.2.75 receptor binding domain complexes revealed the increased thermodynamic stabilization of the BA.2.75 variant and significant dynamic differences between these Omicron variants. Molecular simulations of the SARS-CoV-2 Omicron spike full-length trimer complexes with the ACE2 receptor complemented atomistic studies and enabled an in-depth analysis of mutational and binding effects on conformational dynamic and functional adaptability of the Omicron variants. Despite considerable structural similarities, Omicron variants BA.2, BA.2.75 and XBB.1 can induce unique conformational dynamic signatures and specific distributions of the conformational states. Using conformational ensembles of the SARS-CoV-2 Omicron spike trimer complexes with ACE2, we conducted a comprehensive cryptic pocket screening to examine the role of Omicron mutations and ACE2 binding on the distribution and functional mechanisms of the emerging allosteric binding sites. This analysis captured all experimentally known allosteric sites and discovered networks of inter-connected and functionally relevant allosteric sites that are governed by variant-sensitive conformational adaptability of the SARS-CoV-2 spike structures. The results detailed how ACE2 binding and Omicron mutations in the BA.2, BA.2.75 and XBB.1 spike complexes modulate the distribution of conserved and druggable allosteric pockets harboring functionally important regions. The results are significant for understanding the functional roles of druggable cryptic pockets that can be used for allostery-mediated therapeutic intervention targeting conformational states of the Omicron variants.

Synergy of on-surface and in-air trajectories: Exploratory analysis of forensic online signatures implementing lessons learned from biometrics

With the increased use of digital devices, forensic document examiners (FDEs) encounter increasing number of dynamic or online signatures during their physical examinations. This shift expands the possibility of examinations and creates new challenges for FDEs. As such, FDEs require new examination skills using data science-based analyses with artificial intelligence and machine-learning techniques. In recent years, automated signature verification has gained significant interest in biometric research and could be useful in forensic investigations. However, the use of complex black-box systems inconveniencing FDEs in explaining the rationale behind their final assessment, especially when dealing with limited signature samples and various types of forged signatures. Therefore, a new forensic method is needed to assist FDEs’ analysis. To tackle these challenges and incorporate lessons learned from biometrics into forensics, this study proposes a novel forensic online signature analysis method. The proposed method uses a single-template strategy based on recent scientific findings in biometrics while updating the strategy for forensic use. This strategy creates a mean-template set from known signature samples that serve as a writer’s signature master pattern. Consequently, FDEs can evaluate intra-writer and inter-item variations using the mean-template set and a questioned signature. Furthermore, to take advantage of recent digital devices, we focused on both on-surface and in-air trajectories of online signatures, which could improve the discriminative power because in-air trajectories are invisible for imposters. Finally, we demonstrate the effectiveness and applicability of the proposed method in a forensic scenario using a public forensic online signature dataset.

Dynamic calcium-mediated stress response and recovery signatures in the fungal pathogen, Candida albicans.

Intracellular calcium signaling plays an important role in the resistance and adaptation to stresses encountered by fungal pathogens within the host. This study reports the optimization of the GCaMP fluorescent calcium reporter for live-cell imaging of dynamic calcium responses in single cells of the pathogen, Candida albicans, for the first time. Exposure to membrane, osmotic or oxidative stress generated both specific changes in single cell intracellular calcium spiking and longer calcium transients across the population. Repeated treatments showed that calcium dynamics become unaffected by some stresses but not others, consistent with known cell adaptation mechanisms. By expressing GCaMP in mutant strains and tracking the viability of individual cells over time, the relative contributions of key signaling pathways to calcium flux, stress adaptation, and cell death were demonstrated. This reporter, therefore, permits the study of calcium dynamics, homeostasis, and signaling in C. albicans at a previously unattainable level of detail.

Signatures of Cooper pair dynamics and quantum-critical superconductivity in tunable carrier bands

Different superconducting pairing mechanisms are markedly distinct in the underlying Cooper pair kinematics. Quantum-critical soft modes drive pairing interactions in which the pair scattering processes are highly collinear and can be classified into two categories: forward scattering and backscattering. Conversely, in conventional phonon mechanisms, Cooper pair scattering is of a generic noncollinear character. In this study, we present a method to discern the kinematic type by observing the evolution of superconductivity while adjusting the Fermi surface geometry. To demonstrate our approach, we utilize the recently reported phase diagrams of untwisted graphene multilayers. Our analysis connects the emergence of superconductivity at "ghost crossings" of Fermi surfaces in distinct valleys to the pair kinematics of a backscattering type. Together with the observed nonmonotonic behavior of superconductivity near its onset (sharp rise followed by a drop), it lends strong support to a particular quantum-critical superconductivity scenario in which pairing is driven by intervalley coherence fluctuations. These findings offer direct insights into the genesis of pairing in these systems, providing compelling evidence for the electron-electron interactions driving superconductivity. More broadly, our work highlights the potential of tuning bands via ghost crossings as a promising means of boosting superconductivity.

Dynamic elementomics of single-cell ICP-MS-derived signals in normal and calcium pump PMCA4-deficient mouse epididymal sperm during capacitation.

Currently, clinical analysis of male infertility mainly relies on parameters of semen and sperm cells. However, the high diagnostic failure rates indicate that the current assessment methods are not sufficient and a new approach to evaluating sperm function still needs to be developed. Here we explored the feasibility of single-cell inductively coupled plasma mass spectrometry (sc-ICP-MS)-derived profiles to determine the elemental characteristics in viable capacitated sperm under normal and deficient conditions. To validate the measurements, we used male sterile Pmca4-knockout (KO) mice with impaired calcium clearance, known to be dysregulated due to loss of calcium efflux capacity during sperm capacitation. Consistently, we observed significantly increased calcium intensities in Pmca4-KO sperm upon capacitation stimulation compared with control sperm from the caudaepididymides of wild-type control (WT) mice. More importantly, we explored that the characteristic signatures of calcium intensities in individual spikes derived from sc-ICP-MS was consistent with the dynamics of relative calcium levels in single sperm reported in the literature. Prominent alterations were also observed in the dynamic signatures of sc-ICP-MS-derived profiles of essential elements, particularly the redox-labile elements including copper, iron, manganese, selenium, and zinc in Pmca4-KO sperm compared to WT controls. Therefore, our study demonstrates that elementomics of sc-ICP-MS-derived signals can reveal ionic dysregulation in plasma membrane Ca2+-ATPase isoform 4 protein deficient sperm, and that sc-ICP-MS assay can be applied for functional analysis of viable sperm in functional activities, such as capacitation stimulation. We propose that cell elementomics can be used as an alternative approach to assessing sperm quality and male fertility at the single-cell level.

A study on dynamic group signature scheme with threshold traceability for blockchain

Blockchain technology provides transparency and reliability by sharing transactions and maintaining the same information through consensus among all participants. However, single-signature applications in transactions can lead to user identification issues due to the reuse of public keys. To address this issue, group signatures can be used, where the same group public key is used to verify signatures from group members to provide anonymity to users. However, in dynamic groups where membership may change, an attack can occur where a user who has left the group can disguise themselves as a group member by leaking a partial key. This problem cannot be traced back to the partial key leaker. In this paper, we propose assigning different partial keys to group members to trace partial key leakers and partially alleviate the damage caused by partial key leaks. Exist schemes have shown that arbitrary tracing issues occurred when a single administrator had exclusive key generation and tracing authority. This paper proposes a group signature scheme that solves the synchronization problem by involving a threshold number of TMs while preventing arbitrary tracing by distributing authority among multiple TMs.

Meaning from movement and stillness: Signatures of coordination dynamics reveal infant agency

How do human beings make sense of their relation to the world and realize their ability to effect change? Applying modern concepts and methods of coordination dynamics, we demonstrate that patterns of movement and coordination in 3 to 4-mo-olds may be used to identify states and behavioral phenotypes of emergent agency. By means of a complete coordinative analysis of baby and mobile motion and their interaction, we show that the emergence of agency can take the form of a punctuated self-organizing process, with meaning found both in movement and stillness.

Physical Layer Authenticated Image Encryption for IoT Network Based on Biometric Chaotic Signature for MPFrFT OFDM System.

In this paper, a new physical layer authenticated encryption (PLAE) scheme based on the multi-parameter fractional Fourier transform-Orthogonal frequency division multiplexing (MP-FrFT-OFDM) is suggested for secure image transmission over the IoT network. In addition, a new robust multi-cascaded chaotic modular fractional sine map (MCC-MF sine map) is designed and analyzed. Also, a new dynamic chaotic biometric signature (DCBS) generator based on combining the biometric signature and the proposed MCC-MF sine map random chaotic sequence output is also designed. The final output of the proposed DCBS generator is used as a dynamic secret key for the MPFrFT OFDM system in which the encryption process is applied in the frequency domain. The proposed DCBS secret key generator generates a very large key space of 22200. The proposed DCBS secret keys generator can achieve the confidentiality and authentication properties. Statistical analysis, differential analysis and a key sensitivity test are performed to estimate the security strengths of the proposed DCBS-MP-FrFT-OFDM cryptosystem over the IoT network. The experimental results show that the proposed DCBS-MP-FrFT-OFDM cryptosystem is robust against common signal processing attacks and provides a high security level for image encryption application.

Design of Energy Storage for Frequency Stability in Low-Inertia Power Grid

Short-term frequency instability is one of the major concerns in power systems with high percentage of converter-interfaced renewable energy sources. Energy storage system (ESS) has proven to be a viable solution for the problem of short-term frequency instability by fast frequency response (FFR). However, the appropriate location, size, and operating strategy of ESS are the main challenges for FFR. Power injection at some buses in large grids may lead to angular separation and instability. In addition, oversizing ESS could lead to huge investments without appropriate returns and under sizing may jeopardizes grid stability. Capacity estimation of ESS for FFR considering the suitable location and overall deployment strategies are missing in the current literature for large power grids. Hence, this research proposes a technique to place and size ESS for better FFR in power grids. The proposed technique consists of two steps. In the first step, a methodology based on frequency dynamic signature (FDS) is developed to identify the most suitable location. In the second step, the required capacity of the ESS is estimated based on a step reduction iterative algorithm (SRIA). SRIA and FDS consider the complete dynamics of power system components that affect the frequency dynamics of the system. The proposed methodology is thoroughly verified for various operating conditions in IEEE 39-bus using DIgSILENT PowerFactory.

Linear and Nonlinear Modes and Data Signatures in Dynamic Systems Biology Models

The particulars of stimulus–response experiments performed on dynamic biosystems clearly limit what one can learn and validate about their structural interconnectivity (topology), even when collected kinetic output data are perfect (noise-free). As always, available access ports and other data limitations rule. For linear systems, exponential modes, visible and hidden, play an important role in understanding data limitations, embodied in what we call dynamical signatures in the data. We show here how to circumscribe and analyze modal response data in compartmentalizing model structures—so that modal analysis can be used constructively in systems biology mechanistic model building—for some nonlinear (NL) as well as linear biosystems. We do this by developing and exploiting the modal basis for dynamical signatures in hypothetical (perfect) input–output (I-O) data associated with a (mechanistic) structural model—one that includes inputs and outputs explicitly. The methodology establishes model dimensionality (size and complexity) from particular I-O datasets; helps select among multiple candidate models (model distinguishability); helps in designing new I-O experiments to extract “hidden” structure; and helps to simplify (reduce) models to their essentials. These modal analysis tools are introduced to NL enzyme-regulated and protein–protein interaction biosystems via nonlinear normal mode (NNM) and quasi-steady state approximation (QSSA) analyses and unified with linear models on invariant 2-dimensional manifolds in phase space, with properties similarly informative about their dominant dynamical properties. Some automation of these highly technical aspects of biomodeling is also introduced.

Dynamic cytokines signature predicts survival outcome from severe Immune-related hepatitis with PD-1/PD-L1 blockade in lung cancer

BackgroundImmune-related adverse events (irAEs), particularly immune-related hepatitis (IRH) is a potentially serious complication of immune checkpoint inhibitor (ICI) therapy. This retrospective cohort study investigated potential prognostic and predictive biomarkers for IRH. MethodThis study included 37 patients with advanced lung cancer who received ICIs and were divided into two groups: ≥Grade 3 (G3)-IRH group (n = 17) and without irAE (no-irAE) group (n = 20). Blood samples collected at three different time points and pre-treatment tumor biopsy samples were analyzed using multi-omics assays. ResultsThe IL-1B RNA expression was significantly increased (limma, fold = 1.94) in the ≥ G3-IRH group than the no-irAE group. Compared with no-irAE group, ≥G3-IRH group had higher monocyte and eosinophil infiltration and lower macrophage infiltration, particularly macrophage M2. Transcriptomics analyses of pre-treatment tumor samples revealed significant upregulation of various inflammation-related genes in the ≥ G3-IRH group (False discovery rate < 0.05). Moreover, various proinflammatory cytokines and chemokines were significantly lower in the plasma of the ≥ G3-IRH group than in the no-irAE group. Subgroup analyses of the ≥ G3-IRH group revealed that plasma IL-1A was significantly higher among those whose IRH resolved than those who had IRH-related death. Patients who died had a greater increase in immune score and Euclidean distance from the baseline to the seventh day of IRH onset, with a dramatic increase in Euclidean distance after immunosuppression, suggesting overstimulated immune status. ConclusionOur study demonstrated the association between IL-1B overexpression and IRH susceptibility. Immune score and Euclidean distance of inflammatory cytokines may provide predictive value on the survival outcome from ≥ G3 IRH.

Longitudinal MicroRNA Signature of Conversion to Psychosis.

The emergence of psychosis in ultra-high-risk subjects (UHR) is influenced by gene-environment interactions that rely on epigenetic mechanisms such as microRNAs. However, whether they can be relevant pathophysiological biomarkers of psychosis' onset remains unknown. We present a longitudinal study of microRNA expression, measured in plasma by high-throughput sequencing at baseline and follow-up, in a prospective cohort of 81 UHR, 35 of whom developed psychosis at follow-up (converters). We combined supervised machine learning and differential graph analysis to assess the relative weighted contribution of each microRNA variation to the difference in outcome and identify outcome-specific networks. We then applied univariate models to the resulting microRNA variations common to both strategies, to interpret them as a function of demographic and clinical covariates. We identified 207 microRNA variations that significantly contributed to the classification. The differential network analysis found 276 network-specific correlations of microRNA variations. The combination of both strategies identified 25 microRNAs, whose gene targets were overrepresented in cognition and schizophrenia genome-wide association studies findings. Interpretable univariate models further supported the relevance of miR-150-5p and miR-3191-5p variations in psychosis onset, independent of age, sex, cannabis use, and medication. In this first longitudinal study of microRNA variation during conversion to psychosis, we combined 2 methodologically independent data-driven strategies to identify a dynamic epigenetic signature of the emergence of psychosis that is pathophysiologically relevant.