Search Results (6,170)

Mitochondrial transplantation has been proposed as a strategy to restore cellular bioenergetics after oxidative injury, but the mechanisms governing ATP recovery remain unclear. Using placental mitochondria, we examined ATP restoration following H₂O₂-induced oxidative stress. Unmodified mitochondria modestly increased ATP under baseline conditions but failed to restore ATP after injury. In contrast, lipid-coated mitochondria (MitoCoat) and lipid-encapsulated mitochondria-associated mRNAs (MitoCoat–mRNA) significantly increased ATP levels in injured cells. Transcriptomic analyses revealed that ATP recovery occurred without the normalization of canonical glycolytic or oxidative phosphorylation (OXPHOS) gene programs. Instead, unmodified mitochondria induced broad transcriptional responses associated with immune activation and cellular stress, whereas MitoCoat elicited a more restricted transcriptional profile. Notably, mitochondria-associated mRNAs alone restored ATP without detectable changes in host transcriptional programs. The removal of mitochondrial surface-associated ribosomes or the inhibition of cytosolic but not mitochondrial translation attenuated ATP recovery. The restoration of key metabolic enzymes through cytosolic translation, including PFKP, pyruvate dehydrogenase, and ATP synthase subunit ATP5A suggests that mitochondria-associated mRNAs promote recovery by re-establishing coupling between glycolysis and mitochondrial OXPHOS. Together, these findings identify encapsulated mitochondria-associated mRNAs as a potential strategy to restore cellular bioenergetics under oxidative stress. Full article

Background/Objectives: The STAT (Signal Transducer and Activator of Transcription) family of seven transcription factors mediates cytokine and growth-factor signaling, regulating proliferation, differentiation, and immunity. While STAT3/STAT5 are established oncogenes and STAT1/STAT2 are classically viewed as tumor suppressors, emerging evidence indicates context-dependent roles in tumorigenesis. This study aimed to integrate evolutionary analysis with bulk transcriptomic, regulon, single-cell, and exploratory chromatin-binding analyses of the STAT family in human solid tumors. Methods: Orthologs and paralogs of human STAT genes (81 sequences total) were retrieved across vertebrates and invertebrates; a phylogenetic tree was constructed using MUSCLE alignment and Neighbor-Joining in MEGA12. Differential expression was assessed in TCGA solid tumors versus GTEx normal tissues. Master-regulator activity was inferred using the corto algorithm. Single-cell RNA-seq datasets were used to compare malignant and non-malignant cell populations. STAT1 chromatin binding was examined via ChIP-seq in interferon-stimulated HeLa and K562 cells. Results: Phylogeny resolved seven conserved vertebrate clades, with endocrine-responsive STAT3/STAT5 showing higher conservation and immune-associated STAT1/STAT2/STAT4/STAT6 exhibiting faster divergence. The majority of STAT genes were frequently upregulated across multiple solid tumors, with activated regulons confirming functional transcriptional engagement. Single-cell analysis demonstrated tumor-cell-autonomous upregulation of STAT1 and STAT2 in the HNSCC dataset. STAT1 ChIP-seq revealed asymmetric forward/reverse-strand read density around peak summits, supporting non-canonical DNA recognition. Conclusions: The STAT family operates as an evolutionarily conserved, broadly activated transcriptional module in human solid cancers, combining quantitative upregulation with qualitative shifts in DNA-binding dynamics. These findings refine our understanding of JAK/STAT signaling in oncology and highlight opportunities for network-targeted therapies. Full article

Undifferentiated carcinoma with osteoclast-like giant cells (UCOGC) is a rare, distinct subtype of pancreatic carcinoma, formally classified separately from undifferentiated carcinoma (UC) of the pancreas in the World Health Organization’s 2010 and 2019 revisions. Whereas classic UC is associated with a poor prognosis and low survival rates, recent studies suggest that patients with UCOGC experience significantly longer survival and more frequent diagnosis at surgically resectable stages. Molecular profiling reveals that UCOGC consistently harbors canonical mutations in KRAS, CDKN2A, TP53, and SMAD4, aligning its classification within pancreatic ductal adenocarcinoma. In addition, UCOGC demonstrates a heterogeneous molecular landscape with distinctive mutations of uncertain biological relevance. Immunologically, UCOGC is characterized by a unique tumor microenvironment, notably a deficiency in regulatory T cells (Tregs) and a relative abundance of antigen-presenting cells. Elevated expression of PD-1 within UCOGC further suggests a potential for enhanced response to PD-1-targeted immunotherapies. Collectively, these findings underscore the need for ongoing research into the molecular and immunological characteristics of UCOGC, with the aim of identifying novel biomarkers and developing targeted treatment strategies. Full article

This article offers a theoretically nuanced and empirically grounded investigation into the paradoxical afterlife of classical versification within the poetic practices of the Russian and Soviet avant-garde. Challenging the persistent historiographic narrative that equates avant-garde poetics with an unequivocal rupture from tradition, the study demonstrates that canonical metrical forms—most notably iambic tetrameter—continued to operate as structurally productive, albeit critically reconfigured, elements within experimental verse. Drawing on a broad corpus encompassing poetic manifestos, verse texts, and prose writings by Vladimir Maiakovskii, Ilia Sel’vinskii, Semen Kirsanov, and Nikolai Aseev, the authors combine close formal analysis with quantitative prosodic modeling, including linguistic and speech models derived from Kolmogorov–Taranovsky verse theory. The article argues that avant-garde poets did not simply negate inherited metrics but subjected them to a process of internal recomposition, shifting attention from meter as a fixed scheme to rhythm as a dynamic, semantically charged construct. While rhythmic innovation is shown to be consciously engineered in verse, the analysis of verse-like fragments in prose reveals persistent, unconscious attachments to “classical” rhythmic patterns, particularly the Pushkinian alternating rhythm. This tension between declarative rejection and latent continuity illuminates the avant-garde’s distinctive mode of negotiating tradition: not abolishing it, but instrumentalizing it within a broader project of total artistic reorganization. The study thus reframes avant-garde prosody as a site where innovation and inheritance coexist in a state of productive contradiction, reshaping our understanding of modernist poetic technique. Full article

The Effective Mode Approximation (EMA) is a verification-oriented framework for characterizing collective Hamiltonian dynamics in large continuous-variable (CV) quantum systems from experimentally accessible collective measurements. Rather than reconstructing a full mode-resolved Hamiltonian, EMA maps the observed dynamics onto a canonically normalized collective mode and tests whether summed quadrature trajectories are consistent with an effective harmonic description. We validate EMA using time-resolved homodyne sampling in Gaussian simulations of ring-coupled multi-qu-mode optical systems with $N=8,16,32,$ and 64 modes. One-tone and two-tone sinusoidal models, selected using the Akaike Information Criterion (AIC), recover a stable dominant collective frequency across system size and produce residuals that remain centred near zero. The results show that EMA can verify dominant collective behaviour with a fixed number of effective parameters even when full microscopic reconstruction is impractical. EMA is therefore best understood not as a full-state ansatz, but as a low-overhead tool for validating collective dynamics under realistic measurement constraints in scalable CV hardware. Full article

Denoising one-dimensional signals by wavelet shrinkage critically depends on the choice of wavelet basis, yet basis selection is often guided by heuristics rather than explicit statistical criteria. This paper investigates the relationship between wavelet-basis properties and the shape of the probability density function (PDF) of the detail coefficients in the coarsest retained detail subband. On this basis, it proposes the shape of this PDF as a criterion for wavelet-basis selection. We hypothesize that, for a fixed decomposition depth, noise model, and shrinkage rule, a basis better matched to the signal’s local regularity produces a narrower and more sharply peaked coefficient PDF in this subband than a mismatched basis and can therefore serve as a data-driven indicator for basis selection. To evaluate the consistency of this proposal, we perform controlled hard-thresholding experiments on six canonical test signals, five wavelet bases, and additive white Gaussian noise. Although the test signals differ significantly in local regularity and features, the relationship between basis suitability and PDF shape is confirmed for each of them. Therefore, the results suggest that the proposed PDF-shape criterion is a valuable indicator for wavelet-basis selection. Full article

As multi-view datasets expand across diverse practical fields, feature selection (FS) has become an indispensable preparatory stage for machine learning models. Nevertheless, real-world multi-view data is often unlabeled and distributed among isolated clients, posing significant challenges to traditional centralized methods due to privacy concerns and communication constraints. Furthermore, existing centralized and federated approaches frequently suffer from entrapment in local optima and lack robust convergence guarantees. To address these issues, we propose Fed-MUFSHT, a federated framework for multi-view unsupervised FS (MUFS) that integrates tensor learning with a novel metaheuristic optimizer, Hierarchical-Cognitive Tianji’s Horse Racing Optimization (HC-THRO). Within the federated learning paradigm, Fed-MUFSHT follows a dual-stage local optimization process. Stage 1 applies HC-THRO, which integrates Hierarchical Competitive Learning and Adaptive Cognitive Mapping to simulate multi-level strategic competition and cognitive adaptation among individuals. This design enhances global exploration, adaptive learning, and fine-grained feature selection in high-dimensional spaces. Stage 2 employs a TL module based on canonical polyadic (CP) decomposition to perform missing-view imputation and refine latent representation learning. At the global level, a privacy-preserving aggregation strategy based on Normalized Mutual Information (NMI) and feature weights enables efficient model coordination without exposing raw data. Comparative experiments on several public benchmark datasets reveal that Fed-MUFSHT maintains clear advantages over strong competing methods, showing better optimization results together with more dependable convergence characteristics. The overall evidence suggests that the proposed approach is both robust and effective for distributed optimization tasks involving privacy protection. Full article

Early detection of bearing faults in rotating machinery is essential for ensuring system reliability and effective maintenance planning. Vibration signals inherently contain characteristic fault-related frequency components, providing rich information for both physically interpretable and data-driven analyses. In this study, a multi-representation and correlation-aware feature extraction framework is proposed for automatic classification of bearing faults from vibration signals. Experimental evaluations are conducted using the Case Western Reserve University (CWRU) Bearing Dataset. The dataset includes vibration recordings corresponding to inner race, outer race, ball faults, and healthy conditions under different damage severities. The proposed approach first applies Variational Mode Decomposition (VMD) to suppress noise and enhance frequency-related characteristics. Three different feature representations are then constructed: analytical spectral descriptors, raw Transformer-based deep representations, and a hybrid feature vector obtained by combining these two representations. The hybrid structure is further enhanced through regularized Canonical Correlation Analysis (rCCA), which models the relationship between Transformer representations and spectral descriptors, enabling correlation-aware feature fusion. Spectral, raw Transformer, and rCCA-enhanced hybrid feature vectors are evaluated separately using SVM, Random Forest, and XGBoost classifiers. The results demonstrate that both spectral and Transformer-based representations provide strong performance individually; however, integrating these complementary information sources while modeling their correlations leads to superior and more balanced classification performance. In particular, the rCCA-enhanced hybrid feature vector achieves the best results across all performance metrics. The findings indicate that combining physically meaningful frequency-domain information with data-driven deep representations yields a more robust and generalizable solution for bearing fault diagnosis. Full article

Intracellular calcium (Ca²⁺) signaling is a central regulator of corticogenesis, governing haveneural stem cell behavior, fate transitions, neuronal migration, and circuit assembly. Beyond its canonical role as a second messenger, Ca²⁺ shapes cytoskeletal organization by modulating microtubule dynamics essential for mitotic spindle function, radial glial scaffold, nucleokinesis, and neurite extension. This review synthesizes evidence from in vivo, ex vivo, and in vitro studies to delineate Ca²⁺-dependent pathways and Ca²⁺-binding proteins that couple, within restricted Ca²⁺ microdomains in space and time, to microtubule regulation during mammalian cortical development. We highlight mechanistic nodes involving calmodulin, Ca²⁺/calmodulin-dependent kinases (CaMKs), S100 proteins, cadherins/protocadherins, centrins (CENs), and Ca²⁺ sensors such as STIM1 and calneurons, which collectively coordinate spindle orientation, progenitor division modes, radial migration, and neurite outgrowth. Finally, we discuss how perturbations in Ca²⁺-controlled cytoskeletal programs may contribute to abnormal cortical cytoarchitecture and neurodevelopmental disease. By integrating Ca²⁺ microdomain transients with microtubule control modules, this review provides a unified framework for understanding how Ca²⁺ orchestrates key cellular events during mammalian corticogenesis and propose that Ca²⁺ oscillatory codes are translated into direct or indirect microtubule/cytoskeletal remodeling transitions that determine neural stem cell fate, migration, and maturation, to accurately establish cortical architecture and function. Full article

Osteoporosis is an age-related metabolic bone disorder characterized by an imbalance between bone resorption and formation. Natural polyphenols have gained attention as potential complementary strategies for its prevention. In this study, we investigated the effects of a sustainable, polyphenol-rich extract from red grape pomace (GPE) on human mesenchymal stem cell (MSC) fate and its underlying mechanisms of action. We found that GPE significantly promoted osteogenic differentiation while suppressing adipogenic differentiation in canonical bone marrow-derived MSCs (BMSCs). This biological effect was preserved in adipose tissue-derived MSCs (AdMSCs) obtained from elderly patients (>65 years) at high cardiovascular risk. Mechanistically, GPE downregulated adipogenic markers (PPARγ, CD36 and FABP4) and enhanced osteogenic markers (RUNX2, ALP, OSX, BMP-2, OPN, COL1A1 and OCN). Moreover, GPE activated NRF2-dependent redox signaling, as evidenced by increased NRF2 nuclear translocation and transcriptional activity. Accordingly, GPE treatment significantly upregulated, or consistently increased, the expression of multiple NRF2 target genes, including HO-1, GPX, CAT, GCLC, and NQO1. Importantly, pharmacological inhibition of NRF2 attenuated GPE-induced ALP activity, confirming NRF2 as a key mediator of its osteogenic effects. Overall, grape pomace-derived polyphenols act as upstream modulators of redox-sensitive and osteogenic transcription factors, rebalancing MSC differentiation toward osteogenesis and mitigating age-related bone fragility. Full article

This paper extends the metacybernetic framework by grounding its conceptual descriptions in first principles of information physics. We demonstrate that for living systems to organise efficiently under uncertainty, they must adhere to a strict recursive pattern, a “fractal seed” originating in the third-order interaction between potential and action. By utilising Fisher Information Field Theory (FIFT) within an Informational Realism paradigm, we formalise this process through variational analysis on an implicate–explicate manifold. Under a rigorous informational parsimony constraint (a functional analogue of the holographic principle), we treat the J-field as the dispositional reservoir of latent potential and the I-field as the operative field of structured configurations, and show how their autopoietic coupling generates the system’s Potential–Actuation trait poles as a scale-invariant viability structure This coupling reveals that the boundary substructure, which encodes the holographic content, directly conditions the emergent superstructure through a deterministic parity rule inherited from the dyadic logic of the minimal generic living system represented by ${\widehat{\theta }}_2$. Drawing on the application of Fisher Information, we show that maintaining informational parsimony requires the system’s architecture to oscillate: odd-numbered orders express two traits (dyads), whereas even-numbered orders express three (triads). This produces a canonical 2–3–2–3–2 sequence, preventing a combinatorial explosion of traits as systemic depth increases. We present the Cogitor5 model as a complete fifth-order exemplar of this rule, demonstrating how this rhythmic structural pattern enables self-evolution, systemic coherence, and collective intelligence in both biological and artificial agencies. Full article

Oxidative stress and redox imbalance are central features of both age-related neurodegenerative disorders and the persistent neurological sequelae of coronavirus disease 2019. Increasing evidence suggests that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection disrupts neuronal redox homeostasis via mitochondrial dysfunction, iron dysregulation, inflammatory signaling, and the depletion of pyridine nucleotide pools. In that context, ferroptosis provides a unifying mechanistic framework linking lipid peroxidation to progressive neuronal injury. This review proposes that neuronal vulnerability might depend not only on the oxidative burden itself but also on the failure of membrane-localized antioxidant defenses. Particular emphasis is placed on the plasma membrane redox system (PMRS), a membrane-associated quinone-reducing network that can support coenzyme Q redox cycling and constrain lipid radical propagation at the plasma membrane. Unlike canonical ferroptosis defense systems that rely predominantly on NADPH, components of the PMRS, particularly cytochrome b5 reductase, can also use NADH, conferring partial metabolic flexibility in conditions of redox stress. We further discuss how SARS-CoV-2-induced NAD⁺ depletion might progressively destabilize this membrane-proximal defense architecture, potentially lowering the ferroptotic threshold of vulnerable neurons. Finally, we outline therapeutic strategies that might reinforce PMRS-dependent membrane redox control through NRF2 activation, NAD⁺ restoration, coenzyme Q-centered interventions, and modulation of iron-catalyzed lipid oxidation. Full article

Human–AI systems often involve repeated interaction among users, organizations, and AI components rather than isolated model outputs. In such settings, cooperation can be pursued either by changing agent incentives or by adding an explicit accountability layer. We formalize the Institutional Monitoring and Ledger (IML) framework, which augments a Markov game with monitoring, evidence logging, delayed settlement, and review while leaving the base dynamics unchanged. We derive conservative incentive checks that clarify how detection quality, review accuracy, settlement delay, and sanction size jointly shape deterrence and wrongful-penalty risk. We then provide pilot evidence in two canonical sequential social dilemmas, Harvest and Cleanup, using five agents, PPO training, five training seeds per condition, and comparisons against PPO, inequity aversion, social influence, and IML ablations. In these settings, IML avoided some of the optimization instability observed in the representative internalization baselines tested here, made monitoring error directly visible through ledger records, and showed how false positives can accumulate into a persistent welfare cost. Agent-level analyses in these symmetric environments found nearly uniform measured enforcement burden, while temporal analyses showed that late-stage enforcement is increasingly dominated by residual false positives. These results do not establish legitimacy in human-facing settings or deployment readiness. They instead position IML as a framework with pilot evidence for studying accountability mechanisms in cooperative human–AI systems and highlight measurement error, review design, and due process as central design constraints. Full article

Macrophages harness pattern recognition receptors (PRRs) to detect conserved bacterial components and mount effective immune responses. Many Gram-negative bacteria modify their lipid A structures to limit recognition by Toll-like receptor 4 (TLR4) and cytosolic Caspase-11 lipopolysaccharide sensors. One common evasion strategy is to reduce the lipid A acylation state from hexa- to tetra-acylation. This alteration can limit binding to receptors and dampen subsequent immune signaling responses, yet the proteomic alterations associated with this altered immunogenicity remain incompletely understood. Here, we systematically profiled proteomic alterations induced by extracellular or transfected hexa-acylated Kdo2-lipid A (Kdo2) and tetra-acylated lipid-IVa (IVa) to assess TLR4-dependent, TLR4-independent, and non-canonical inflammasome activation pathways. Kdo2 elicited stronger inflammatory responses in immortalized bone-marrow-derived macrophages (iBMDMs), as evidenced by robust TNF production, Caspase-11 cleavage, and IL-1α/IL-1β release. In contrast, IVa elicited minimal TNF secretion and failed to effectively induce non-canonical inflammasome activation. Global label-free quantitative proteomic analysis of iBMDMs stimulated with a low dose of immunogenic LPS displayed route-specific immune signatures: enrichment of TNF signaling, interferon-associated pathways, and mitochondrial metabolic remodeling. Equimolar amounts of low-acylated LPS failed to effectively induce these immune signatures, supporting a threshold-dependent model in which the lipid A structure and route of exposure define inflammatory progression. Collectively, our findings provide mechanistic insight into how lipid A structural variation modulates macrophage immune programming and cytosolic inflammasome activation. Full article

The Landauer principle motivates the definition of economic temperature as the monetary price of processing a bit irreversibly. No empirical test of this definition exists in transparent fee markets. This paper fills that gap using daily Bitcoin and Ethereum data, constructing canonical thermodynamic state variables and evaluating five diagnostic layers: state variable behavior, Maxwell-type integrability, Carnot-style efficiency bounds, nonlinear regime separation, and structural break sensitivity to protocol events. Bitcoin’s log-temperature behaves as a persistent mean-reverting process with an AR(1) coefficient of 0.97 and a half-life of 21 days; Ethereum is highly persistent, with weaker formal evidence of stationarity than Bitcoin. Maxwell integrability is frequency-dependent: Bitcoin passes all four relations at monthly frequency, whereas Ethereum passes two of four. Carnot-style evidence is the strongest: realized fee extraction efficiency stays well below the implied bound, with daily compliance exceeding 97% on both chains. Structural breaks around Bitcoin ordinals, EIP-1559, the merge, and Shanghai confirm that protocol changes reorganize the temperature relation. The thermodynamic framework provides structure that standard fee market analysis does not, including a first principles efficiency bound and a state space coherence test. The findings provide partial, frequency-dependent, and chain-specific empirical support for a Landauer-based thermodynamic description of blockspace markets. Full article