Metabolic Control of Immunity and Inflammation: Mitochondrial Dynamics, Pharmacological Targets, and Therapeutic Opportunities.

This paper presents an adaptive optimal consensus tracking control scheme for canonical nonlinear multi-agent systems (MASs) with unknown dynamics, employing an actor–critic reinforcement learning (RL) framework. The scheme integrates a sliding mode mechanism to suppress tracking errors and ensure consensus tracking between the followers and the leader. Additionally, optimal control is designed to find a Nash equilibrium in a graphical game. To address the intractability of obtaining an analytical solution for the coupled Hamilton–Jacobi–Bellman (HJB) equation, a policy iteration algorithm is utilized. Within this algorithm, a critic neural network (NN) approximates the gradient of the optimal value function, while an actor NN approximates the optimal control policy. Together, these networks form a compact actor–critic (AC) architecture that achieves optimal consensus tracking. Furthermore, the proposed method guarantees the boundedness of all closed-loop signals while ensuring consensus tracking. Finally, two simulations are conducted to verify the effectiveness and advantages of the proposed method.

Many of the regulatory systems for controlling cell envelope biogenesis and stress responses have yet to be studied. Here we characterize a Clostridioides difficile BlaIR-like regulatory system that we have named CenIR for cell envelope. Unlike canonical BlaIR systems, which bind β-lactams and induce a β-lactamase, CenIR lacks a β-lactam binding domain and is essential for viability even in the absence of antibiotics. We identified the genes in the regulon and found that CenIR is essential because its absence leads to overproduction of the Cwp6 peptidoglycan hydrolase. We also show that most annotated BlaIR-like systems lack a β-lactam-binding domain, from which we infer that these systems have much broader physiological roles than generally appreciated.

Abstract This study addresses the probability distribution of real complex physical systems by deriving the incomplete statistical distribution for a canonical ensemble under constraints of energy and volume, using the maximum entropy principle. Numerical simulations are conducted to validate the approach, providing a novel methodology for further exploration of power-law distributions in real-world complex systems.

Chromatin structure plays a central role in regulating transcription, genome stability and epigenetic inheritance in eukaryotes. Much of our understanding of chromatin architecture and histone post-translational modifications (hPTMs) comes from a narrow set of animal and plant models, but emerging data from non-model lineages are challenging canonical views of how chromatin functions across the tree of life. Brown algae are complex multicellular eukaryotes that provide a unique perspective on chromatin evolution given their independent origin of complex multicellularity. Here we compile the chromatin toolkit of brown algae and show that canonical silencing systems involving DNA cytosine methylation and Polycomb repressive complex 2 (PRC2)-mediated histone H3 lysine 27 (H3K27) methylation were lost early in their evolution. By generating hPTM profiles from diverse brown algal clades, we resolve the nature and regulatory roles of chromatin states in this lineage and show how H3 lysine 79 (H3K79) methylation emerged and diversified as a repressive system. We further uncover sex-specific reconfigurations in species with varying degrees of sexual dimorphism and reconstruct the ancestral regulatory landscape that probably preceded the emergence of brown algae. Together, our findings illuminate the dynamic evolution of chromatin regulation in a distinct multicellular lineage and challenge assumptions about the universality of chromatin-based mechanisms across eukaryotes.

Macroautophagy is an evolutionarily conserved degradation pathway in eukaryotes that mediates the turnover of cytoplasmic components. The formation of autophagosomes, a hallmark of autophagy, involves autophagy-related (Atg) proteins, including two ubiquitin-like conjugation systems, Atg12 system and Atg8 system. In most species, Atg12 covalently binds Atg5, forming the Atg12-Atg5-Atg16 complex that functions as an E3-like enzyme to promote Atg8 conjugation with phosphatidylethanolamine (PE), a step essential for autophagosomal maturation. By contrast, certain species such as yeast Komagataella phaffii lack Atg10 and/or the C-terminal glycine of Atg12, relying instead on a non-covalent Atg12-Atg5 complex. However, the physiological significance of this reductively evolved non-covalent system and its divergence in molecular mechanisms from species harbouring the canonical covalent Atg12 system remain undiscussed. In this study, we demonstrate that under nitrogen starvation, KpAtg12 is phosphorylated and lipidation of KpAtg8 is enhanced. Our results with a phosphorylation-deficient mutant of KpAtg12 suggest that KpAtg12 phosphorylation modulates the activity of nitrogen starvation-induced macroautophagy through KpAtg8 lipidation reaction.

This article addresses the consensus tracking control problem for a class of unparametrizable noncanonical nonlinear multi-agent systems. Most existing consensus tracking control schemes for nonlinear multi-agent systems (MASs) are restricted to canonical-form system models. For noncanonical-form nonlinear MASs, to our best knowledge, there is still no result available so far due to some challenging technical issues encountered in design and analysis. Firstly, existing distributed observers are limited to canonical systems by the assumption that the output matrix of leader is known, rendering them inapplicable to noncanonical nonlinear systems. Moreover, current adaptive distributed observer frameworks only consider first-order differentiability of the system matrix, missing the information of higher-order derivative. Secondly, unlike the traditional Lyapunov method for canonical systems, noncanonical systems require augmented error analysis for stability, and extending this to noncanonical nonlinear MASs is highly challenging. Thirdly, unparametrizable nonlinear functions in the original system complicate control design. To tackle these issues, an approximate Takagi-Sugeno (T-S) fuzzy models are constructed to eliminate non-parametric functions, alongside a novel adaptive distributed observer for estimating leader information. Simulations verify the effectiveness of our control scheme in achieving consensus control tracking.

Entamoeba histolytica lacks a canonical glutathione-based antioxidant system and primarily relies on the thioredoxin pathway for redox homeostasis, making peroxiredoxin (EhPrx) a critical vulnerability and potential therapeutic target. Through structure-based screening, Dinaciclib was identified as a novel direct inhibitor of EhPrx. Pharmacological targeting of EhPrx by Dinaciclib triggers a ferroptosis-like cell death pathway characterized by oxidative stress and significant accumulation of intracellular iron and lipid peroxides. In contrast, the well-established ferroptosis inducer RSL3 did not induce lipid peroxidation but instead upregulated EhPrx expression, suggesting an adaptive defense mechanism against indirect oxidative stress. Transcriptomic analysis following Dinaciclib treatment revealed widespread dysregulation of genes central to ferroptosis, including those involved in iron, lipid, and cysteine metabolism. Collectively, our results suggest that targeting EhPrx overwhelms the parasite's primary redox defense, leading to iron-driven oxidative cell death. Thus, this study identifies EhPrx as an exploitable target and proposes a distinct mechanism that triggers ferroptosis-like death in E. histolytica.

Adaptive filtering via canonical systems with time-varying hamiltonians

Erratum to An inverse problem for a class of canonical systems and its applications to self-reciprocal polynomials

The chlorine dioxide-iodine-malonic acid (CDIMA) reaction governed by the Lengyel-Epstein equations is a canonical reaction-diffusion system known for generating rich Turing patterns and oscillatory behavior. This study implements RBFNNs to analyze and predict the spatiotemporal dynamics of the CDIMA reaction. The coupled nonlinear partial differential equations describe the evolution between iodide and chlorite ion concentrations whose interactions drive the emergence of spatial patterns. Radial basis function neural networks (RBFNNs) are employed to approximate these complex nonlinear processes which offers a data-driven approach to modeling both reaction kinetics and diffusion. RBFNN successfully learns to reproduce concentration profiles and capture pattern formation under varying reaction rates and diffusion coefficients. This method reduces computational cost while providing insights into parameter settings where analytical solutions are intractable. The accuracy of network is assessed through regression analysis across training, validation, testing, and overall datasets with the correlation coefficient R measuring agreement between predicted and true values. R-value of 1 represents a perfect correlation between predictions and targets demonstrating the ideal performance of the model. This study highlights the potential of RBFNN as a powerful tool for studying complex chemical systems bridging the gap between theoretical models and experimental observations in nonlinear dynamics.

Apicomplexan parasites like Toxoplasma gondii harbor a highly divergent mitochondrial proteome, much of which remains uncharacterized despite its essentiality for parasite survival. One such critical pathway is ubiquinone (UQ) biosynthesis. Here, we characterize the UQ synthesis machinery in T. gondii and show that conserved enzymes, TgCoq3 and TgCoq5, are essential for growth and mitochondrial function, forming a multi-protein complex. Using proximity labeling and subcellular fractionation, a strategy suited for detecting proteins of low abundance, we identify TgCoqFAD, a unique FAD-dependent monooxygenase required for UQ synthesis. Unlike canonical eukaryotic systems that employ multiple monooxygenases to modify specific carbons on the UQ aromatic ring, TgCoqFAD catalyzes two distinct hydroxylation steps, an activity not previously reported in eukaryotes. Molecular docking and chemical screening identified TgCoqFAD inhibitors that impair tachyzoite growth and bradyzoite viability. These findings reveal a streamlined and divergent UQ biosynthesis pathway in apicomplexans and establish TgCoqFAD as a promising antiparasitic target.

Histone variants define distinct chromatin states by modulating the biophysical properties of nucleosomes. Variants play a particularly important role in the parasitic protist Trypanosoma brucei, which has unusual chromatin and lacks a canonical repressive heterochromatin system. Instead, T. brucei utilizes specialized divergent histone variants H3.V and H4.V. However, the biochemical basis of their repressive functions is unknown. Here, we determined the structure of the H3.V-H4.V nucleosome core particle and biochemically characterized variant-containing nucleosomes and nucleosome arrays, probing their unique properties. We discovered that surprisingly for repressive-state nucleosomes, H3.V promotes pronounced DNA splaying, largely via its N-terminal tail region, while retaining overall stability that is comparable to canonical nucleosomes. In contrast, H4.V exhibits near-identical binding to DNA but mediates a slight increase in histone octamer stability. The surface of the H3.V-H4.V nucleosome is altered and provides a differential platform for chromatin-binding proteins, linking the variants to parasite pathogenicity.

Genome-wide computational screening and in vitro assays reveal a non-OBP chemosensory system in Botrytis cinerea responsive to plant triterpenoids.

Mitochondrial membrane potential (ΔΨm) is a critical component of the protonmotive force that drives ATP synthesis and supports essential mitochondrial functions, including metabolite transport, ion homeostasis, and protein import. In the parasitic protist Trypanosoma brucei, ΔΨm regulation is uniquely adapted across life cycle stages to meet changing metabolic demands. In the insect-stage procyclic form (PF), ΔΨm is generated by a canonical electron transport system (ETS), while in the bloodstream form (BF), where complexes III and IV are absent, ΔΨm is maintained by the reverse operation of ATP synthase, consuming ATP to pump protons. In T. brucei evansi, which lacks mitochondrial DNA, the ATP synthase is unable to translocate protons, and ΔΨm is sustained solely by electrogenic ADP/ATP exchange through the mitochondrial carrier. This chapter presents three complementary fluorescence-based methods to evaluate ΔΨm in T. brucei and T. b. evansi cells, highlighting their applicability to both intact and permeabilized parasites. We detail the use of two ΔΨm-sensitive dyes-TMRE, a cell-permeable dye suited for live-cell assays, and Safranine O, used in permeabilized preparations-and describe protocols for flow cytometry and fluorescence spectroscopy, respectively. These approaches allow robust, qualitative and semi-quantitative analysis of ΔΨm under different metabolic and experimental conditions. We address specific challenges associated with using fluorescent dyes to measure ΔΨm including issues of dye concentration, cellular permeability and potential artifacts that can affect interpretation of ΔΨm measurements.

A central challenge in consciousness research concerns the relationship between neural activity and conscious experience. While decades of work have identified numerous neural correlates of consciousness, these findings increasingly indicate that activation magnitude, localization, or stimulus processing alone are insufficient to account for awareness. What remains less clearly articulated is how neural activity is organized in association with conscious experience. In this review, we synthesize empirical findings that bear on the structure of neural states correlated with conscious experience. Drawing on research in neural correlates of consciousness, representational similarity analysis, neural manifold studies, perturbational approaches, and canonical sensory systems, we examine how neural activity is organized in state space across conscious and unconscious conditions. Across paradigms and measurement modalities, conscious experience is consistently associated with neural states that are restricted to admissible configurations, organized within low-dimensional subspaces or manifolds, structured by meaningful geometric and topological relationships, and dynamically accessible under perturbation. We show that distances in neural representational space track experiential similarity, that categorical perceptual distinctions correspond to clustering and boundaries in neural state space, and that perturbational measures distinguish accessible experiential states from inactive or fragmented configurations. Rather than advancing a specific theory of consciousness, this review provides a unifying structural synthesis that clarifies empirically grounded constraints on the neural organization associated with conscious experience.

Selective eIF4E-eIF4G Pairing and Cap-4 Recognition Mechanisms in Trypanosomatids: Insights From EIF4E5-EIF4G1 and EIF4E6-EIF4G5 Complexes.

Composite pulses (CPs) are widely used in nuclear magnetic resonance (NMR), optical spectroscopy, optimal control experiments and quantum computing to manipulate systems that are well-described by a two-level Hamiltonian. A careful design of these pulses can allow the refocusing of an ensemble at a desired state, even if the ensemble experiences imperfections in the magnitude of the external field or resonance offsets. Since the introduction of CPs, several theoretical justifications for their robustness have been suggested. In this work, we suggest another justification based on the classical mechanical concept of a stability matrix. The motion on the Bloch sphere is mapped to a canonical system of coordinates and the focusing of an ensemble corresponds to caustics, or the vanishing of an appropriate stability matrix element in the canonical coordinates. Our approach highlights the directionality of the refocusing of the ensemble on the Bloch sphere, revealing how different ensembles refocus along different directions. The approach also clarifies when CPs can induce a change in the width of the ensemble as opposed to simply a rotation of the axes. As a case study, we investigate the 90(x)180(y)90(x) CP introduced by Levitt, where the approach provides a new perspective into why this CP is effective.

The precise engineering of electromagnetic couplings is paramount for constructing scalable and high-fidelity superconducting quantum processors. While essential for orchestrating qubit operations, these couplings also present significant design challenges, including the mitigation of crosstalk and the management of environmental decoherence. A clear and unified theoretical framework is therefore crucial for the design, simulation, and analysis of these complex quantum circuits. This paper presents a comprehensive theoretical treatment of the fundamental electromagnetic coupling mechanisms in superconducting devices. Starting from first principles, we formulate the equations of motion and derive the input-output relations for canonical systems, including a single resonator coupled to a multi-port microwave network, interacting resonators, and coupled transmission lines. We review rigorous definitions for key parameters such as the energy decay rate (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa$</tex-math></inline-formula>) and the dimensionless coupling coefficient (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\zeta$</tex-math></inline-formula>) and connect these formalisms to practical methods of parameter extraction from electromagnetic simulations. This work provides a rigorous and pedagogical foundation for understanding and modeling linear electromagnetic interactions, serving as a vital resource for the development of advanced superconducting quantum hardware.

Abstract This work develops and rigorously analyzes reduced‐order modeling (ROM) techniques for the time‐dependent Schrödinger equation (TDSE), with the goal of efficiently capturing essential quantum dynamics at significantly reduced computational cost. Three major ROM frameworks–Proper Orthogonal Decomposition (POD), Dynamic Mode Decomposition (DMD), and Reduced Basis Methods (RBM) are explored and compared–in the context of quantum wavefunction evolution. Comprehensive mathematical formulations are presented, including projection‐based Galerkin approximations, a priori and a posteriori error estimates, stability analyses, and convergence guarantees. Numerical experiments are conducted for canonical quantum systems such as the infinite square well, harmonic oscillator, and tunneling through potential barriers, as well as a time‐dependent controlled two‐level system. It is demonstrated that ROMs can achieve orders‐of‐magnitude dimensionality reduction while maintaining high fidelity with full‐order model (FOM) solutions. Furthermore, the framework is extended to higher‐dimensional problems, nonlinear potentials, and multi‐particle systems, with applications in quantum control and entanglement dynamics. Visualization of ROM accuracy through mesh, surface, and isosurface plots, as well as convergence studies, confirms the robustness of the proposed methods. To support reproducibility and further research, all MATLAB code used to generate the numerical experiments is made publicly available via GitHub. These results establish ROM as a powerful tool for real‐time simulation, control, and optimization in computational quantum mechanics.