Biology and Life Sciences

Abstract: The linear–quadratic (LQ) model accumulates dose but carries no internal state, so it cannot represent how an earlier delivery conditions the response to a later one. We propose that radiation response is better treated as the trajectory of a single coupled state resolved into five layers ordered by relaxation time — redox, damage-stress signal, escape gate, radiation-tolerant persister, and population — read out through one kill kernel of which LQ is the exact frozen-state limit. Because the layer clocks separate by orders of magnitude the architecture is reducible: eliminating the fast layers leaves a two-compartment core of the lagged write that installs tolerance and the tolerance itself, on which the system is solved exactly. The resulting closed-form surviving fraction becomes independent of the entire delivery history on exactly two loci: an ablation locus, following from a left-eigenvector identity holding at every instant and therefore exact, all-orders, and independent of both the tolerance functional and the write; and an instantaneous-write locus, where the write becomes proportional to the kill. Cell-autonomy of the write is precisely the condition under which a closed form exists, and the ablation null survives even where it does not. Specialised to a fraction train under a delayed write, the same equation derives a survival floor previously added to LQ by hand; fitted to data at five fraction sizes it returns a consolidation window of 1.75 fractions and is preferred over the phenomenological alternative in sample, though not under held-out fraction sizes. The remaining results require layers the core does not contain, which is the argument for the framework and not only for the theorem. The falsifiable content is structural: a matched-dose, order-reversed contrast abolished by persister depletion.

Abstract: Personalized glioma neoantigen vaccination requires a defensible way to prioritize candidate peptides when mutation, HLA presentation, biochemical, structural, manufacturability, and provenance signals are incomplete or discordant. We present TAMAVAQ/Q-TAMAVAQ, an audit-first computational framework that converts candidate triage from an opaque rank list into a replayable constrained decision process. Candidate dossiers are embedded with a Fubini-Study-style fidelity kernel, connected through a symmetrized 12-nearest-neighbour graph, and assigned CTQW neighborhood support. A deterministic MBHA boundary ledger records signed eligibility margins and preserves non-compensatory BIO, PHYS, GEOM, CMC, PROV, and VALID requirements. In the internal worksheet described in the source manuscript, the analysis contains n = 1,537 candidate records and a locked 65-candidate evidence-positive endpoint (4.23%). The full selector achieved AP = 0.552, AUROC = 0.962, and NDCG@100 = 0.693, compared with AP = 0.505 for graph-transport support alone and AP = 0.515 for a non-graph linear comparator. These findings establish internal computational feasibility and auditability only; biological validation remains prospective. The final proof schematic consolidates the quantum geometry, CTQW transport, PASS oracle and MBHA margin ledger into a single theorem-linked audit description.

Abstract: Cryogenic technologies have become an important catalyst for the development of sciece in the 20th century. They have played a significant role in biology (preservation of animal and plant gene pools and biological samples) and medicine (cryosurgery and cryotherapy). In photobiology, the use of deep cooling has deepened our understanding of the mechanisms of key processes: photosynthesis, vision, and photoregulation in plants. This review, based on the author's work, focuses on the characteristics and results of applying deep cooling methods in spectrofluorimetry of several major photoreceptor pigments. Two key effects of object cooling—a sharp narrowing of pigment spectral bands and an increase in their fluorescence quantum yield—have enabled, firstly, advances in the description of the multicomponent system of chlorophyll, phycobilins, and carotenoids, as well as the processes of light energy harvesting during photosynthesis. The system of chlorophyll forms and the energy migration between them and from accessory pigments are reproduced in model systems with aggregated pigments, indicating the important role of pigment-pigment interactions in the formation of the system of forms and during primary photoprocesses within them. Secondly, the fluorescence of visual and bacterial rhodopsins, and of phytochrome in its native state was detected in vivo; their primary photoprocesses were studied, and their general scheme was proposed. Structural and functionally distinct types of phytochrome A were discovered. The advantages of the application of cryotechnology in the spectroscopy of complex biological pigment systems are discussed.

Abstract: Is an ant colony conscious? What about a culture, an AI, or a planet? A conscious moment feels unified in time and space, yet signals take time to travel. Many theories bind an experience's parts within a window of duration θ, but leave other questions about time and space unanswered. Here I give a mathematical tool for answering some of those questions, narrowing down what is conscious under different theories. There are two possibilities, Arpeggio and Chord. Arpeggio says consciousness requires only that each ingredient occur somewhere in-window. Chord is harder to satisfy, requiring ingredients coexist in time and influence one another. This limits the size of conscious unity. For a system of diameter D and signal-speed ceiling v, its exchange architecture sets a factor ε, giving D ≤ εvθ. Primate data constrain θ. Chord can augment the likes of Orch-OR, IIT 4.0 and GWT with size bounds, time constraints and two-way exchange requirements. Within Stack Theory, Chord completes the temporal aspect of the Psychophysical Principle of Causality. Given a theory and parameters Chord can, for example, rule out ant colonies or certain AI, or give latency limits for human–AI hybrids. In contrast, under Arpeggio all theories collapse toward panpsychism.

Abstract: Nuclear magnetic resonance (NMR) spectroscopy quantifies metabolites across many biological matrices, but in one-dimensional spectra of complex biofluids such as urine and plasma, extensive signal overlap obscures individual metabolite signals and complicates their quantification. Resolving these overlaps by deconvolution is only the first step: turning a set of spectra into a statistically analysable table also requires aligning corresponding signals across samples and condensing them into a feature matrix, a path that has typically been stitched together from several separate tools and is both cumbersome and time consuming. We present metabodeconplus, an R package that unifies this entire path into a single reproducible end-to-end workflow. From raw one-dimensional spectra, it deconvolutes overlapping signals as Lorentzian line shapes, automatically aligns the resulting peaks across samples, and condenses them into a data matrix of aligned signal integrals ready for built-in sample classification or downstream statistics. Automated parameter optimization removes manual tuning, and a Rust computational backend with parallelization reduces runtime substantially over the deconvolution-only predecessor MetaboDecon1D, from which the workflow is extended. The package is freely available as open source on GitHub and is currently under review at CRAN. metabodeconplus thus lowers the barrier from raw NMR spectra to reproducible metabolomic analysis.

Abstract: Prostate cancer is a primary cause of cancer-related deaths in men which requires its rapid and accurate identification to improve treatment outcomes. This research aimed to develop an innovative, accurate and non-invasive diagnostic framework for the early detection of prostate cancer by combining laser-induced breakdown spectroscopy (LIBS) with machine learning. Prostate cancer and healthy samples were examined using LIBS to analyze their spectra. Elements like calcium, nitrogen and sodium along with the CN-band showed a higher concentration in cancer samples as compared to healthy ones. Various machine learning models were trained to accurately discriminate between cancer and healthy samples. Among several machine learning models, a trilayered neural network achieved the highest training accuracy of 85.0% and the linear SVM model achieved the highest prediction accuracy of 80.0%. Compared to other traditional methods, LIBS emerged as a robust and reliable technique. This study demonstrated the strong potential of combining LIBS with machine learning for the detection of prostate cancer. The technique not only enhanced the diagnostic accuracy but also reduced the need for invasive procedures showing promise for broader medical use.

Abstract: Recently, novel nanobiotechnological approaches have been developed to reduce the impact of pollutants during the synthesis of nanoparticles (NPs). To do so, green synthesis methods have now become widely adopted, using biomolecules to produce high-quality, stable nanoparticles. In this work, we employed the K1 toxin produced by S. cerevisiae as a reducing and capping agent for silver nanoparticles. The synthesis of Ag-K1 NPs was carried out using a concentrated protein fraction from the culture medium of S. cerevisiae 42300 containing the secreted K1 toxin. The obtained nanoparticles were characterized using UV-Vis spectroscopy, STEM, EDS, and FTIR, and the antimicrobial efficacy was determined against S. cerevisiae, P. aeruginosa, and B. subtilis. The synthesized NPs showed high antimicrobial efficacy, killing all tested strains; additionally, dose-response modeling suggested differential activity among the nanoparticles. This work reports, for the first time, the biosynthesis and antimicrobial characterization of silver nanoparticles associated with K1 killer toxin-containing protein fractions.

Abstract: Relevance. The solvent accessible surface area (SASA) of amino acid residues is a key characteristic for protein structure analysis, but precise methods for calculating it (e.g., FreeSASA) are computationally expensive. Empirical approximations based on the res-idue interaction network (RIN) graph can provide high speed while maintaining ac-ceptable accuracy. Proposed approach. Three empirical functions for estimating relative SASA are pro-posed: approx_sasa, surface_score, and exp_sasa using the degree of the node in RIN as an argument. We present a comparative study of two approaches to graph construction: the classical Cα-graph (threshold 8 Å) and the graph of heavy atoms (Heavy-Atom Graph, HAG, threshold 5.0 Å). The parameters were calibrated on a sample of 509 protein structures (128,794 residues) from various origins using the true relative SASA calculated by the FreeSASA library. Main results. An extended set of 11 RIN topological features was developed and vali-dated, including basic node characteristics, centrality measures (betweenness, eigen-vector, closeness) and hydrophobic subgraph features. Training ensemble models (Random Forest, XGBoost) with these features made it possible to achieve: Random Forest on HAG: MAE = 0.057 ± 0.033, Pearson r = 0.915 ± 0.080 (best result), Random Forest on Cα graph: MAE = 0.066 ± 0.041, Pearson r = 0.890 ± 0.100. Comparison with GNN. We compared our approach with graph neural networks (GCN, GAT, GraphSAGE). GraphSAGE on HAG showed a result close to Random Forest: MAE = 0.0715, Pearson r = 0.8917, indicating the potential applicability of graph neural net-works when using HAG. GCN and GAT performed significantly worse (MAE = 0.14–0.15, Pearson r = 0.51–0.61). Computational efficiency. Empirical formulas are calculated in 0.008 ms per structure (~26,000× faster than FreeSASA), Random Forest in prediction mode is calculated in 36.5 ms (~6× faster than FreeSASA). HAG construction takes 21 times longer than a Cα graph (279.5 ms vs. 13.3 ms). Practical significance. The proposed empirical features are recommended for large-scale pipelines critical to speed and interpretability. Random Forest on HAG is the optimal choice for tasks that require maximum accuracy (MAE = 0.057, Pearson r = 0.915). GraphSAGE on HAG can be considered as an alternative when using deep learning.

Abstract: Humans routinely offload cognitive tasks to their environments. Here we show, employing just basic physics and the Free Energy Principle, that all time-persistent information-processing systems offload information-processing tasks to their environments. Hence all cognitive systems engage in cognitive offloading. We show how ecological niche construction, kinematic replication, bioelectric signaling, the development of communication systems based on shared semantics, and the ability of LLMs to demonstrate fluent language use in the absence of extra-linguistic input all exemplify this offloading process. We conclude that both theoretical understanding of problem-solving abilities and the engineering of such abilities into artifacts will be improved by considering active computation by the environment as a ubiquitous adjunct to cognition in both living and artificial systems.

Abstract: Syntaxin-1 (Stx1), a core neuronal SNARE protein, regulates membrane docking and fusion during neurotransmitter release. Although Stx1 clustering is widely observed at presynaptic active zones and in neuroendocrine cells, how Stx1 isoforms and lipid interactions organize the plasma membrane prior to fusion remains unclear. Here, we performed MARTINI coarse-grained molecular dynamics (cgMD) simulations of 1–10 copies of the juxtamembrane and transmembrane domains (JMD–TMDs) of Stx1A and Stx1B embedded in plasma membrane models to investigate membrane lipid disorder, Stx1 JMD–TMD clustering dynamics, and regulation by Stx1 TMD palmitoylation and phosphatidylinositol 4,5-bisphosphate (PIP2). Stx1 JMD–TMD regions induced local lipid disorder primarily through hydrophobic mismatch, with Stx1A producing stronger and more spatially extended perturbations than Stx1B. The magnitude of local lipid disorder remained largely independent of clustering and TMD palmitoylation, whereas palmitoylation spatially restricted and PIP2 depletion broadened the radial extent of membrane perturbation. Stx1 proteins spontaneously formed protein-density-dependent oligomers, with PIP2 depletion and palmitoylation suppressing higher-order oligomerization. Together, these findings support a model in which hydrophobic mismatch, palmitoylation, and PIP2 cooperatively regulate membrane organization and Stx1 clustering, thereby modulating transitions toward membrane environments permissive for SNARE-mediated fusion.

Abstract: Artemisia annua L. (A. annua) produces artemisinin, a key antimalarial compound whose biosynthesis is strongly influenced by light. Although the effects of light inten-sity and spectral quality on plant growth and secondary metabolism have been widely studied, plant responses to extremely low photon fluxes remains poorly understood. This study investigates growth and metabolic responses in A. annua exposed to coher-ent ultralow-photon illumination generated from attenuated red (R) and violet-blue (VB) lasers, compared with plants grown under natural sunlight over a 14-day treat-ment period. The attenuated laser treatments delivered photon flux densities several orders of magnitude lower than those of natural sunlight. Height increment differed among treatments, with the VB treatment produced the greatest increase in height (39.9%, p < 0.01), followed by the R treatment (32.4%, p < 0.05), and natural sunlight (23.0%). In contrast, plants grown under natural sunlight exhibited the greatest in-crease in leaf area. Estimated artemisinin concentrations were highest under natural sunlight (31.09 mg g⁻¹ DW), lower under the R treatment (6.98 mg g⁻¹ DW), and lowest under the VB treatment (1.14 mg g⁻¹ DW). These findings indicate that extreme photon limitation promotes stem elongation while strongly suppressing artemisinin accumu-lation. Overall, the results demonstrate measurable growth and metabolic responses under ultra-low photon fluxes and provide new insight into the interaction between light availability, spectral quality, and growth–metabolite trade-offs in medicinal plants.

Abstract: Scaffold architecture profoundly influences tissue regeneration through the mechanical, topographical, and biochemical cues it presents to cells. Yet the relationship between architectural complexity and regenerative performance remains difficult to interpret: geometrically elaborate scaffolds do not necessarily produce more organized tissues, while comparatively simple architectures can exert strong organizational effects. We propose that scaffold-guided tissue organization is better understood through a distinction between geometric complexity and informational dimensionality. We argue that scaffold performance depends less on geometric complexity than on the number of stable, biologically interpretable dimensions available for cellular mechanotransduction. Drawing heuristically on kernel methods in machine learning, we suggest that scaffold architectures can transform poorly structured mechanosensory environments into conditions where alternative organizational trajectories become more distinguishable. Mechanotransduction provides the biological basis for this process, integrating scaffold-derived cues through focal adhesions, cytoskeletal tension, nuclear deformation, and YAP/TAZ signaling. This perspective suggests that scaffold design should be evaluated not only by architectural complexity or biomimetic resemblance, but by its capacity to generate stable and interpretable mechanosensory environments. More broadly, this shifts the design question from how complex a scaffold is to whether its architecture generates stable, cell-readable mechanosensory information.

Abstract: Background/Objectives: Gamma-aminobutyric acid (GABA) is increasingly recognized as an important modulator of pancreatic beta-cell function, but the mechanisms by which it regulates intracellular Ca²⁺ oscillations and coordinated beta-cell activity remain insufficiently understood. The aim of this study was to investigate how GABA influences the amplitude, frequency, phase adjustment, entrainment, and synchronization of beta-cell Ca²⁺ oscillations. Methods: We developed an extended mathematical model based on our previously established Dual Anaplerotic Model of the GABA shunt. The model incorporates explicit dynamics of cytosolic Ca²⁺, endoplasmic reticulum Ca²⁺, ATP, and a regulatory variable controlling Ca2+ influx, while extracellular GABA is represented as a delayed interstitial signal feeding back on cellular excitability. Single-cell and two-cell simulations were performed to analyze GABA-dependent oscillatory regulation and intercellular coupling. Results: The model reproduced key experimental observations under both control and GABA-deficient conditions, including reduced Ca²⁺-oscillation amplitude and prolonged oscillation period when GABA production was suppressed. Mechanistically, GABA affected single-cell oscillations through two complementary pathways: metabolically, by modulating ATP production through PEP-related and TCA-related contributions linked to the GABA shunt; and extracellularly, by adjusting the phase of Ca²⁺ influx through fast and delayed inhibitory feedback. In the two-cell model, delayed interstitial GABA signaling was sufficient to entrain and synchronize non-identical oscillators over finite ranges of parameter mismatch, and weak effective electrical coupling further broadened these synchronization ranges. Conclusions: GABA acts as a dual regulator of beta-cell dynamics, linking intracellular metabolism to Ca²⁺-oscillation patterning and promoting coordinated activity through intercellular phase adjustment. The model provides a mechanistic framework connecting GABA metabolism, ATP dynamics, Ca²⁺ signaling, and beta-cell synchronization in pancreatic islets.

Abstract: Circadian disruption resulting from factors such as jet lag, shift work, or aging leads to exaggerated inflammatory responses and increased disease susceptibility. However, the core dynamical mechanism by which circadian disruption exacerbates innate immune responses remains poorly understood. Here, we develop an integrated mathematical model coupling the mammalian circadian clock with antigen-induced innate immune responses, incorporating key regulatory interactions including glucocorticoid modulation and pro-inflammatory positive feedback loops. The model successfully recapitulates experimental data regarding homeostatic immune circadian oscillations and time-dependent gating of acute inflammatory responses. Dynamic analyses reveal that the circadian clock exerts its gating function by modulating the bistable characteristics within pro-inflammatory positive feedback loops. Circadian disruption, simulated as jet lag or age-related reduction in clock gene amplitude, reshapes this bistable landscape and prolongs residence duration in the pathological hyperinflammatory state. This shift not only amplifies acute cytokine bursts but also sustains exaggerated inflammatory activity, providing a unifying mechanistic explanation for acute tissue injury and chronic low-grade inflammation (inflammaging) under circadian disruption.

Abstract: Protein-based biosensors require controlled and site-selective functionalization strategies to enable stable immobilization and signal transduction without compromising protein structure and activity. Here, we evaluate a chemoselective linchpin-directed modification (LDM) approach targeting Lys–His pairs as a tool for site-specific labeling of the model fluorescent biosensor green fluorescent protein (GFPmut2). A panel of LDM molecules with variable spacer lengths was prepared, and a structure-guided computational workflow was implemented to map Lys–His distances on the protein and predict potential modification sites. Experimental validation by UV–Vis spectroscopy and mass spectrometry demonstrated efficient conjugation and a final degree of labeling close to unity, consistent with single-site modification, with all LDM molecules selectively targeted the same histidine residue (His181), independently of spacer length. Structural analysis revealed that this residue is located within an accessible internal cavity, enabling a funneling effect that enhances local reactivity. Importantly, the modification preserves the fluorescence and pH response of GFP, confirming retention of sensing functionality. These results demonstrate that LDM enables selective modification not only of surface residues, but also of structurally guided, non-surface residues. This approach provides a novel strategy for the controlled functionalization and immobilization of protein-based biosensors, improving their stability and performance.

Abstract: The kinetic-bystander framework of McMahon et al. (2013) remains the most rigorous formal model of signalling between irradiated and unirradiated cells, but three residual gaps remain. First, the intracellular response of recipient cells is compressed into a single hazard parameter μ, leaving unspecified the molecular processes linking signal reception to altered behaviour. Second, the framework contains no explicit mechanism for persistent recipient-cell state through which prior signalling exposure modifies future response across fractions. Third, recent structural identifiability analysis showed the model to be intrinsically non-identifiable under conventional surviving-fraction observation, limiting recoverability of internal dynamics from observable response. Subsequent extensions broadened biological scope through immune-mediated cohort and abscopal effects (Asur et al., 2015; Moghaddasi et al., 2022; Jenkins et al., 2024) and improved phenomenological fit through dose–distance interaction terms (Arous et al., 2025), but did not address these residual intracellular and observational limitations. We propose, as a hypothesis, that the AP-1 / CBP-p300-mediated cis-epigenetic memory mechanism recently demonstrated by Li et al. (2026) provides one candidate intracellular implementation addressing the first two gaps while partially enriching the third through additional observables. We support the proposal through three analyses requiring neither numerical simulation nor new data: a timescale-reconciliation argument with explicit treatment of pulsed versus continuous exposure, structural propositions yielding parameter-independent predictions, and literature-derived order-of-magnitude analysis. We further connect the proposal to existing phenomenological extensions by predicting how the empirical interaction parameter of Arous et al. (2025) should vary with AP-1 / CBP-p300 status and fraction number. We position the mechanism as complementary to organism-level immune-memory processes, operating at a different biological scale through distinct machinery. The framework generates experimentally testable pharmacological predictions while remaining explicitly hypothetical and awaiting direct validation.

Abstract: Time-resolved neutron scattering has been used to study dynamically polarised protons in tyrosyl doped bovine liver catalase. Both dynamic nuclear polarisation and the efficiency of the reversal of the proton polarisation change significantly with the occupancy of the tyrosyl radical inside the catalase molecule. A sample rich in tyrosyl (0.78 per heme) is compared with earlier data from a sample with much lower occupancy of 0.58. An extremely localized proton polarisation of unprecedented height near each of the tyrosyl radicals is maintained by an efficient magnetic nuclear spin diffusion barrier. The proton polarisation remains low further away from the tyrosyl radical sites.

Abstract: This study aimed to characterize metabolomic changes in the eye lens of senescence-accelerated OXYS rats in comparison with control Wistar rats, and to identify biochemical shifts associated with genotype, age, and cataract progression. Cataract severity was clinically graded. Rats' lenses were analyzed using quantitative ¹H NMR spectroscopy at 3.6 and approximately 4.5 months of age. A total of 43 metabolites were quantified. We found that at 3.6 months of age, OXYS lenses exhibited a significant accumulation of 17 metabolites, primarily amino acids, compared to Wistar rats, suggesting an imbalance between amino acid uptake and crystallin biosynthesis. However, by 4.5 months, OXYS lenses exhibited rapid metabolic changes characterized by significant decreases in amino acid, glucose, and key energy/antioxidant markers, including NAD, adenylate energy charge, and hypotaurine. Clinical cataract grade (Grade 2 vs. 3) had a negligible impact on the overall metabolomic profile. Our results indicate that profound metabolic reorganization, including an initial amino acid excess followed by energy and antioxidant depletion, precedes the morphological manifestation of cataracts in OXYS rats. We suggest that a biochemical "point of no return" occurs early in cataractogenesis, while subsequent increase in lens opacification is a secondary consequence of preexisting metabolic disturbances.

Abstract: The association of small molecules with lipid membranes plays a central role in drug delivery, pharmacokinetics, toxicity, and membrane biophysics, also being of fundamental importance in drug pharmacodynamics given that most drug targets are membrane-associated proteins. Accurate determination of solute–membrane association affinities, however, remains challenging due to the diversity of experimental systems, the complexity of membrane environments, and the intrinsic limitations of individual methodologies. This review provides a comprehensive overview of the experimental and computational approaches currently used to quantify small molecules association with lipid membranes. Standard experimental techniques, including spectroscopy-based methods, calorimetry, electrophoretic measurements, and surface-sensitive approaches, are discussed alongside established computational strategies ranging from continuum models to atomistic molecular dynamics simulations. Particular emphasis is placed on the formalisms required for data analysis, including partitioning models and thermodynamic frameworks, as well as on the assumptions underlying each method. The validity limits, sources of uncertainty, and common experimental and interpretative pitfalls are critically examined. By providing a unified and comparative perspective, this work establishes a structured framework for the quantitative study of solute–membrane interactions, guiding new researchers in the selection of appropriate methodologies and in the rigorous analysis of experimental and computational results. Moreover, it enables the consistent and quantitative rationalization of affinity parameters reported across the literature, supporting the development of curated datasets and predictive relationships that can inform the design of new and more effective drugs.

Abstract: Rel/SpoT family enzymes participate in controlling the cellular levels of the alarmone (p)ppGpp, thereby activating the stringent response and promoting survival under stress conditions. These proteins contain an N-terminal catalytic domain and a C-terminal regulatory domain. They catalyze both the synthesis of ppGpp/pppGpp from ATP and GDP/GTP and their hydrolysis to GDP/GTP and pyrophosphate. Here, we report the crystal structure of the N-terminal domain of Rel from Streptococcus equisimilis (Rel_Seq385) in complex with pppGpp at 3.2 Å resolution. The asymmetric unit contains a dimer with asymmetric ligation, in which pppGpp occupies only the synthetase site in one monomer, whereas it is observed in both the hydrolase and synthetase sites in the other. Molecular dynamics simulations supported this binding arrangement for the monomer with both sites occupied and revealed additional probable transient binding sites that may contribute to alarmone binding.