Abstract: A neural model for the formation of visual engrams is proposed, operating according to a non-Hebbian principle — specifically, through the enhancement of inhibitory synapses, up to and including the formation of veto synapses. The model relies on two hypothetical mechanisms: (1) rapid, repetitive reactivation ("ripple-reverberation") and (2) high-frequency synchronization enabling the activation of inhibitory synapses, which consequently become veto synapses. Through such learning, "neural locks" for familiar patterns are formed in memory. This model constitutes a component of a more general top-down model of visual recognition described previously (Levashov & Safiulina, 2025). The problem of processing activity patterns in living neural networks is discussed, as these patterns are not holistic but rather manifest as a mosaic of activated and non-activated neurons.

Abstract: Background: Lung cancer causes more deaths than any other malignancy worldwide, accounting for 2.2 million new cases and 1.8 million deaths in 2020. Extracting structured clinical knowledge from unstructured French-language oncology records remains methodologically unresolved in Tunisian and Francophone healthcare systems, where validated natural language processing tools do not yet exist. This study examined the effectiveness of transformer-based named entity recognition for automated clinical annotation of Tunisian lung cancer reports. Aim: The study aimed to (i) benchmark four transformer-based models on a publicly available thoracic radiology dataset, (ii) evaluate five models, including a French biomedical specialist, on a newly constructed Tunisian clinical corpus, and (iii) demonstrate prototype deployment feasibility for structured clinical decision support. Methods: A benchmarking study evaluated BERT, RoBERTa, BioClinicalBERT, and CamemBERT on the RadGraph dataset (600 annotated thoracic radiology reports). Five models were subsequently fine-tuned on 200 manually annotated initial diagnostic reports from Mami Pneumo-Phthisiology Hospital, Tunis. All models were trained for a maximum of 10 epochs, with a learning rate of 5x10-5, a batch size of 16, and an 80/10/10 train-validation-test split, and evaluated using precision, recall, and F1-score. Results: On RadGraph, RoBERTa achieved the highest F1-score of 0.873 (precision: 0.869, recall: 0.877), followed by BioClinicalBERT (F1: 0.868) and BERT (F1: 0.857). CamemBERT achieved an F1 score of 0.682 on this English dataset. On the Tunisian corpus, DrBERT outperformed all models with an F1-score of 0.811, compared to RoBERTa at 0.79. A prototype interface generated structured clinical summaries encompassing prior conditions, imaging modalities, and TNM staging. Conclusion: Language- and domain-adapted transformer models effectively extract structured clinical entities from French-language Tunisian lung cancer reports. DrBERT's precision advantage confirms that biomedical pretraining in the target language is the primary driver of performance in specialized French oncology text. This work establishes foundational infrastructure for NLP-driven oncology data management in Tunisia and comparable Francophone settings.

Abstract: In hepatocellular carcinoma (HCC), aberrant histone modifications are linked to the dysregulation of long non-coding RNA (lncRNA) expression. Although existing computational models can accurately predict some associations, they lack deep physical interpretability. We constructed an energy model based on the physical principle that energy determines molecular structure. Total DNA segment energy was calculated by summing adjacent trinucleotide interaction energies and applied to analyze 11 key histone modifications in HCC, specifically within lncRNA promoter regions where modification signals were increased or decreased. Finally, ten-fold cross-validation revealed that significant energy differences between sequences with increased and decreased histone signals enable excellent classification performance. These results indicted a strong correlation between the total energy of local DNA structures and histone modification signal. Furthermore, introducing longer k-mers led to computational redundancy without a consistent improvement, confirming that the trinucleotide model most effectively acquires the local DNA structural changes associated with histone modification levels. Our model can effectively distinguish DNA sequences associated with different histone modification levels from a physical energy perspective. This model serves as an interpretable tool for epigenetic research while providing a new understanding a new perspective for understanding the dysregulation of lncRNA expression in HCC.

Abstract: Pressure-powered spore-launching strategy is a unique feature for many fungal species. Fungal species have evolved this dispersal method to project spores into the surrounding environment, thereby increasing reproductive success and completing their life cycle. Sporangia squirters (like Pilobolus), ballistospore catapulters (like Agaricus), and ascospore launchers (like Neurospora) are prominent fungal cannons. Osmotic pressure and surface tension are responsible for propelling spores at extreme acceleration exceeding 20,000g and velocities reaching 94 km/h, thus enabling spores to travel distances of up to several meters. Thanks to advances in biophysical modeling and high-speed imaging, the century-old mystery of fungal launching strategies has been understood to involve principles of fluid mechanics, optics, and projectile dynamics, by investigating many fungal models. With a focus on the underlying biophysical principles and their broader implications for fungal ecology, this review summarizes current knowledge of morphology and biomechanics. Additionally, it discusses how each step of spore launching relates to fundamental physical principles of energy and motion.

Abstract: Lineages are conceived as corridors through time of two-sigma exclusion of uncertainty by Bayesian analysis of numbers of morphological traits and species. Methods of structural monophyly constructed evolutionary dendrograms of a family and of a tribe of bryophytes. Shannon informational bits known for speciation events in these groups allow projections of changes into the past. Sampling and relationships in the present imply kinds and rates of origination and extinction of species. Vopson’s estimates of the mass of one informational bit is used to spacelike calculate total mass of information of two bryophyte lineages from total bits associated with speciation over time. Three levels of observational sampling yield similar information masses for the two lineages. Numbers of observations of calculated events over time are expressed in “Reals” as fundamental units of historical reification. Observation is postulated as a source of historical reification based on a functional equivalence of switching of light-speed-mediated Then and Now for estimation of spacelike events. This Common Now concept is quite like that of a many worlds theory of entangled events. Dark matter, Schrödinger’s cat, aliens, the Big Bang, the Matrix, and dragons make an appearance. Best fit to average axion depth is observational sampling at 2 cm size of a mathematically spherical moss. Scientific reality is identified with calculated information extending experience in both directions of time.

Abstract: The trajectories of complex biological systems are commonly inferred from long-term observations of recovery or deviation after perturbation. We suggest that early-time state-space geometry could contain information enough to anticipate system trajectories before recovery. This hypothesis is informed by extensions of the quantum adiabatic theorem suggesting that under fast, nonadiabatic perturbations, a system prepared in its ground state within the same phase retains the largest overlap with the post-perturbation ground state. Translating to biological systems, we consider cellular functional identity as a stable attractor in a high-dimensional state space where abrupt perturbations like brief inflammatory pulses do not induce regime transitions. Our simulations suggest that post-perturbation states distribution is biased toward the original attractor, reflecting persistence of structural alignment rather than uniform exploration of accessible configurations. Early-time overlap with the baseline attractor, attractor dominance and state-space entropy could stand for operational metrics for inferring system fate. Higher initial overlap should correspond to increased return probability and reduced dispersion, whereas reduced overlap may indicate proximity to regime boundaries. We predict that system fate can be inferred from initial post-perturbation configurations without requiring long-term observation. Potential applications of our framework include fast assessment of cellular resilience, early identification of instability preceding disease transitions and optimization of intervention strategies based on early system responses.

Abstract: Lists of “unsolved mysteries” in genetics, human origins and consciousness often mix (i) genuine mechanistic unknowns, (ii) limitations of measurement and reference quality, and (iii) category errors in which philosophical questions are treated as if they were missing molecular details. This review reframes each frequently cited “mystery” as an explicit evidence-gap and evaluates it against current data and explicit null models. A conservative information-theoretic framing is used throughout: genomes, cells, brains and societies are treated as physical systems that store, transform and transmit information under thermodynamic and evolutionary constraints. This framing helps distinguish what is open in practice (because measurement is hard) from what is constrained in principle (because population genetics, chemistry and neural dynamics limit the plausible solution space). We synthesize evidence on: (1) noncoding DNA and the “junk DNA” debate, separating biochemical activity from selected function; (2) protein folding in vivo, emphasizing energy landscapes, cotranslational folding, chaperones and quality control, and clarifying what machine-learning structure prediction does—and does not—explain; (3) Y-chromosome evolution and why complete telomere-to-telomere assemblies shift arguments about degeneration and disappearance into quantitative population genetics; (4) epigenetic inheritance, robust within individuals but constrained across mammalian generations by germline reprogramming; and (5) “dark genome” claims as annotation and callability problems increasingly addressed by long-read assemblies, proteogenomics and ribosome profiling. For human origins, we revisit chromosome-scale rearrangements (including the chromosome 2 fusion), ancient DNA evidence for branching histories and admixture, and misconceptions about “mitochondrial Eve” and the “missing link.” For abiogenesis, we articulate an experimentally anchored chain from plausible prebiotic synthesis to nonenzymatic copying and protocell growth/division, while acknowledging unresolved bottlenecks (error thresholds, sustained cycles, and metabolism–genetics coupling). Finally, we evaluate quantum-level claims about consciousness with a stringent burden-of-proof: quantum biology exists in specific systems, but strong proposals in neuroscience must specify physical carriers, coupling mechanisms, coherence/error-correction arguments, computational advantages, and discriminating perturbation tests. We conclude with a falsifiability battery and an evidence hierarchy designed to separate productive hypotheses from untestable narratives.

Abstract: The continued evolution of SARS-CoV-2 has enabled escape from most monoclonal antibodies, yet a subset of broadly neutralizing antibodies targeting three newly identified super-conserved RBD epitopes—SCORE-A, SCORE-B, and SCORE-C—retains remarkable activity against even the most recent JN.1-derived sublineages. Here we employed an integrated computational framework combining conformational dynamics, mutational scanning, MM-GBSA binding energetics, and frustration profiling to dissect the molecular mechanisms by which XGI antibodies achieve broad neutralization and resistance to immune escape. Structural analysis revealed that all three SCORE epitopes share a common architecture: a highly conserved, minimally frustrated core that provides stable anchoring, flanked by peripheral regions that accommodate antibody-specific variations. Conformational dynamics showed that SCORE-A antibodies (XGI-183) rigidify the lateral epitope while leaving the RBM partially mobile; SCORE-B antibodies (XGI-198, XGI-203) clamp the RBM apex, directly blocking ACE2; and SCORE-C antibodies (XGI-171) allosterically loosen the RBM loop, impairing receptor engagement indirectly. Mutational scanning identified a hierarchical hotspot organization where primary hotspots (e.g., K356, T500, Y380, T385) are evolutionarily constrained and minimally frustrated, while secondary hotspots (e.g., V503, Y508, S383) are neutrally frustrated and represent the principal sites of immune-driven mutations. MM-GBSA decomposition revealed that van der Waals-driven hydrophobic packing dominates binding, with electrostatic interactions providing auxiliary stabilization. Critically, frustration analysis demonstrated that immune escape hotspots reside precisely in zones of neutral frustration—"energetic playgrounds" that permit mutational explora-tion without destabilizing the RBD—while minimally frustrated cores are evolutionarily locked. The comparative analysis of conformational versus mutational frustration dis-tributions revealed a unifying principle: aligned neutral frustration yields permissive, escape-prone interfaces; decoupling enables targeting of constrained cores; and convergence of minimal frustration in both distributions creates invulnerable interfaces. These findings establish that broad neutralization arises not from ultra-high-affinity anchors but from strategic energy distribution across rigid, evolutionarily informed interfaces, providing a roadmap for designing next-generation therapeutics that target the invulnerable cores of viral surface proteins.

Abstract: The axonal membrane is not the seat of nerve conduction: it is the boundary between two osmotic reservoirs whose asymmetry is the thermodynamic engine of the action potential. Voltage-gated ion channels are not the generators of the nerve signal -- they are its osmotic amplifiers, and their spatial distribution along the axon is a geometric necessity, not an arbitrary anatomical feature. The Ionic-Mechano-Hydraulic (IMH) model formalises this principle: intracellular K$^{+}$ adsorbed on the cytoplasmic polyelectrolyte gel triggers an ionic phase transition; extracellular Na$^{+}$ amplifies the resulting hydraulic wave via Nav channels; Kv channels close the osmotic cycle and enforce the refractory period. Conduction velocity is predicted from myelin elastic modulus, not sodium channel density. The model resolves a 75-year-old anomaly that Huxley and St\"{a}mpfli themselves described as impossible in a purely electrical system: positive current enters a node before the membrane potential at the preceding node has reached its maximum. Nine falsifiable predictions are presented -- among them, a graded reduction in conduction distance under partial tetrodotoxin block, a bell-shaped relationship between node length and conduction velocity, and an upper diameter limit for unmyelinated fibres derived from first physical principles. The Hodgkin-Huxley model is not discarded: it is explained.

Abstract: Fighting against gravity is a common challenge for all terrestrial animals, including most mammals. It means, in particular, avoiding falls on the ground while performing daily tasks, such as standing up, locomotion or foraging for food. This means that balance control in humans involves a wide variety of contexts and balance paradigms, such as upright standing, hand-standing, tightrope walking, ice skater spinning, bicycling, whole-body gesturing, and stick balancing on a finger,tip among others. From the cybernetic point of view, the underlying control problem is to keep the CoP (Center of Pressure) and the CoM (Center of Mass) aligned dynamically on the common vertical, and this means that the variety of balance strategies can be reduced to two basic paradigms: the CoP strategy (the CoP is the control variable and the CoM is the controlled variable) and the CoM strategy (the CoM is simultaneously the control and the controlled variable). The two balance strategies are implemented by combining different control paradigms: • Opportunistic control: exploitation of a physical phenomenon as the gyroscopic effect. • Stiffness control: exploiting the elastic properties of skeletal muscles. • Feedback control (continuous or intermittent): measuring an incipient fall index and closing the loop in real-time. • Feedforward control: exploiting an internal body model for generating stable whole-body synergies in an anticipatory manner. Such control paradigms are illustrated with experimental and simulated experiments.

Abstract: Background: genistein and resveratrol are bioactive compounds isolated from plants, recognised for their diverse biological activities including anti-cancer properties. Both compounds are also known as modulators of potassium channels, including the Kv1.3 ones. These channels are expressed in both normal and cancerous tissues. Their activity is crucial in regulating cell proliferation and apoptosis in cells that express Kv1.3 channels. The potential clinical application of channel inhibitors may extend to treating cancers characterized by an over expression of Kv1.3 channels. Methods: this study investigates the inhibitory effects of genistein and resveratrol on Kv1.3 channels in cancer cells – human leukemic Jurkat T cells, applying the whole-cell patch-clamp technique. Results: applying both compounds at concentrations ranging from 3 μM to 90 μM leads to a dose-dependent inhibition of the channel activity, reducing it to approximately 50% of control level. This inhibitory effect was reversible and associated with a significant reduction of the activation rate. When combined with simvastatin, the inhibitory effect exhibited synergy; however, it was additive when co-applied with mevastatin. Conclusion: the inhibition of Kv1.3 channels is likely linked to the anti-cancer activities of these compounds on Kv1.3 channel- expressing cancer cells, especially when co-applied with the statins.

Abstract: Molecular dynamics (MD) simulation is a fundamental technique for resolving biomolecular structures and functions at atomic resolution. Accelerated by GPU computing and machine learning-integrated force fields (FF), modern MD simulation facilitates the study of large-scale systems and rare biological events, such as protein folding, allosteric transitions, etc. While advanced sampling methods and AI integration have significantly enhanced efficiency in drug discovery and protein engineering, the field still faces challenges regarding FF accuracy, timescale constraints, and quantum effects. Continued development of hybrid quantum and molecular mechanics methods and standardized workflows is essential to further improve the predictive power and reproducibility of MD in biotechnological research. In this review, we attempted to provide the latest developments in the MD simulations.

Abstract: Accurate assessment of blood viscosity and red blood cell (RBC) aggregation under continuous flow is important for hemorheological analysis. However, simultaneous measurement remains challenging because both properties are influenced by flow conditions and RBC sedimentation. In this study, a microfluidic method is developed for the simultaneous measurement of blood viscosity and RBC aggregation index (AI) during continuous blood delivery from a driving syringe. The proposed device consists of a viscosity-sensing channel for viscosity measurement and aggregation-sensing channel for AI evaluation. The effects of flow rate, hematocrit, suspension medium, and syringe on-off operation are systematically investigated. Blood viscosity and AI are strongly affected by these factors and transient flow interruption enhances RBC sedimentation in the syringe, thereby altering hemorheological properties. The pro-posed method is further used to thermally shocked RBCs which reduce RBC aggregation and suppress RBC sedimentation when compared with control blood. At higher exposure temperatures and longer exposure times, blood viscosity and AI remain nearly constant over time, indicating minimal contribution of damaged RBCs to RBCs sedimentation. These results demonstrate that the proposed method enables reliable simultaneous evaluation of blood viscosity and RBC aggregation and could be regarded as useful for detecting functional alterations of RBCs under continuous-flow conditions.

Abstract: Blood viscosity is strongly dependent on hematocrit, and the hematocrit–viscosity relationship is an important determinant of blood rheology under physiological and pathological conditions. However, obtaining a full hematocrit–viscosity curve requires multiple measurements over a wide hematocrit range. In this study, a simple method is proposed to reconstruct the full hematocrit–viscosity curve using only three-dataset Krieger–Dougherty (K–D) regression as μ=μ0(1-ϕϕm)-α ϕm. Based on suspended blood, RBC-rich blood and RBC-depleted blood are prepared after centrifugation. Hematocrit of each blood is measured using a micro hemocytometer. Simultaneously, blood viscosity of each blood is measured using coflowing streams method. The proposed method is evaluated sequentially using reference datasets and hematocrit-viscosity datasets of control blood. According to results, full hematocrit–viscosity curve obtained from selected three datasets is in well agreement with the experimental data and yields lower root-mean-square error than conventional method using all datasets. The exponent of K–D model is strongly influenced by the midpoint dataset whereas μ0 is mainly affected by suspending medium (dextran solution). In contrast, GA-induced rigidified RBCs do not significantly affect μ0. In conclusion, the proposed method provides simple, efficient, and reliable approach for estimating the full hematocrit–viscosity curve.

Abstract: Non‑thermal millimeter‑wave (MMW) irradiation (75–110 GHz) represents a promising non‑invasive strategy for cancer therapy. Lung cancer remains the leading cause ‎of cancer‑related mortality worldwide, highlighting the need for alternative therapeutic modalities that can overcome resistance and minimize toxicity. Yet the effects ‎of MMW exposure in physiologically relevant 3D systems remain insufficiently ‎characterized. Here, we evaluated the anti‑cancer efficacy of MMW exposure in 3D ‎lung cancer spheroids (NCI‑H1299, A549) alongside noncancerous WI‑38 fibroblasts. ‎Cells were irradiated using two antenna types—a waveguide (WG; localized, ‎high-power density) and a pyramidal horn (PH; broader coverage, lower power ‎density)—with or without a frequency multiplier to modulate local energy delivery. ‎Acute responses were assessed by XTT viability assays (day 2) and apoptosis (flow ‎cytometry), while chronic effects were evaluated using clonogenic survival (day 10) ‎and senescence markers. ‎ MMW exposure reduced cancer cell survival in a time‑ and power‑dependent manner ‎and induced sustained growth inhibition. Apoptosis was markedly higher in cancer ‎cells than in non‑cancerous WI‑38 cells and was further amplified under power-enhanced conditions. WG irradiation produced strong localized antiproliferative ‎effects, whereas the PH antenna enabled broader coverage while maintaining selective cytotoxicity toward NCI‑H1299 cells. Notably, p53‑deficient NCI‑H1299 cells ‎exhibited up to ~64% apoptosis after 60 min of exposure, whereas WI‑38 fibroblasts ‎remained below ~20%, demonstrating robust cancer selectivity. These findings high-‎light the selective, non‑thermal anticancer potential of MMW irradiation in 3D tumor ‎models and provide a mechanistic and experimental foundation for further preclinical ‎optimization of MMW‑based therapeutic strategies.

Abstract: This article addresses a current point of contention in the field of single molecule/single particle tracking, as well as relevant literature, and supplements it with some published cell-based experiments to illustrate our conclusions and known theorems. We attempt to explain the controversy surrounding the differing biophysical and cell biological results of studies on the individual molecule and those “at the single-molecule level” as well as at the level of many molecules in such a way that even readers who are unfamiliar with the subject can understand it without having to read all the mathematical, physical, and biophysical references. Given this abundance of studies in the literature, it is obvious that genuine single-molecule studies are urgently needed, i.e., single-molecule studies that focus on increasing the sensitivity of the temporal resolution of single-molecule measurements and not just on spatial resolution.

Abstract: One of the most puzzling and unsolved challenges in molecular biology is understanding how proteins fold. Despite having advanced predictive tools that can accurately estimate the native structures of proteins, we still lack a comprehensive model that explains how amino acid sequences dictate folding pathways and trajectories. This manuscript introduces a novel treatment for the issue by employing the “principle of least action.” This approach enables us to explore an intriguing question: how does a protein achieve its native state at a constant folding rate and within a biologically plausible time frame? A response to this inquiry will help us understand why proteins must fold along specific pathways and identify the boundary conditions that limit their availability. Furthermore, the principle of least action—together with the effective trajectory conjecture—enables us to explain why different proteins could exhibit the same folding rate. Finally, it will enable us to provide an in-depth description of the genesis and solution of Levinthal's paradox. Our results are expected to pave the way for a more profound understanding of how proteins fold, shedding light on how the amino acid sequence and its surrounding environment encode the protein's folding pathways and, consequently, the protein's three-dimensional structure.

Abstract: Current efforts in improving photodynamic therapy focus on nanomaterials that integrate deep-tissue imaging with efficient reactive oxygen species generation. Gold nanoclusters (Au NCs) are promising alternatives to conventional photosensitizers due to their effective ROS production and enhanced biocompatibility when stabilized by protein corona. However, both photosensitizers and Au NCs are typically activated by ultraviolet or visible light, which cannot penetrate deeper into tissues and is limited to superficial applications. Here, we report a near-infrared (NIR)-activated photodynamic nanoplatform based on core-shell upconverting nanoparticles (UCNPs; NaGdF₄:Yb³⁺,Er³⁺@NaGdF₄:Yb³⁺,Nd³⁺), functionalized with a protein corona containing bovine serum albumin-stabilized Au NCs (BSA-Au NCs) and photosensitizer chlorin e6 (Ce6). Spectroscopic data confirmed the formation of the UCNP-BSA-Au-Ce6 nanoplatform and demonstrated 32% energy transfer efficiency from UCNPs to Ce6, resulting in efficient reactive oxygen species generation under 808 nm irradiation. Cellular experiments confirmed effective internalization and optimal biocompatibility of the nanoplatform in human breast cancer and healthy cells. Upon 808 nm irradiation, the nanoplatform significantly reduced viability of MDA-MB-231 cancer cells. These findings indicate that the UCNP-BSA-Au-Ce6 nanoplatform couples NIR activation with enhanced singlet oxygen production, providing a multifunctional platform for deep-tissue imaging and NIR-activated photodynamic therapy.

Abstract: The method of X-ray Footprinting and Mass Spectrometry (XFMS) using high brightness synchrotron X-ray sources has become an established method in structural biology and is based on the radiolytic production of hydroxyl radicals which oxidatively modify protein sidechains. While other methods of producing hydroxyl radicals are available, one benefit of using high flux density sources is that hydroxyl radical scavenging reactions can be minimized, and exposure times kept short to minimize secondary reactions. Here we present an application of the XFMS method using low dose rate X-rays from a commercial instrument. We demonstrate the feasibility of the approach using short peptides, characterizing the oxidative modifications +14, +16, and +32 Da under both aerated and low-oxygen conditions, and we additionally quantify the hydrogen peroxide production for various doses using the low dose rate source. These results provide fundamental information on the oxidative damage to peptides due to hydroxyl radicals using a low dose rate X-ray source.

Abstract: Biological systems display phenomena—particularly in enzymatic catalysis, excitonic coherence, and protein folding—that appear to exploit selective stabilisation of microstates beyond what standard quantum mechanics typically predicts for warm, noisy environments. We propose that these deviations can be interpreted as signatures of an informational reservoir: a hidden, aperiodic layer of structured information accessible only to sufficiently complex biological systems. Standard quantum mechanics then emerges as a limiting, coarse‑grained description in which the reservoir term vanishes. The proposed reservoir is not reducible to any finite set of underlying parameters; instead, it functions as a high‑complexity information landscape that can be “read” only by finely organised biomolecular architectures. We outline empirically testable predictions and discuss implications for biological stability, functional directionality, and the physical foundations of living systems.