The thalamus plays a crucial role in motor control but its complex circuitry remains poorly understood. Existing computational models often lack anatomical detail, hindering investigations on how structure influences function. Here we present a data-driven 3D anatomical scaffold model (a geometrically constrained virtual circuit) of mouse motor thalamic nuclei, built by integrating publicly available datasets, anatomical descriptions, geometric constraints and circuit-level findings to reproduce topographical organization and structural boundaries observed experimentally. Network simulations demonstrate sustained spindle oscillations at physiologically realistic frequencies, with propagation velocities matching observations from thalamic slices. Systematic ablation reveals that both topography and distance-dependent synaptic weights are necessary for physiological dynamics, establishing architectural design principles for spatially organized circuits. These findings generalize beyond the motor thalamus: the open-source pipeline provides a reusable framework for data-driven circuit reconstruction, linking anatomical organisation to emergent network dynamics across brain regions.

A Systematic Review of Computational Models and Future Directions on Multi-Physics Interactions and Heat Transport Phenomena in Micropolar Systems

Everyday experiences can evoke positive feelings that differ among individuals and guide their behavior. Although reward processing is often linked to positive feelings, the mechanisms underlying subjective positive affect and whether they are shared for varied positive experiences is unclear. Here, we used fMRI and predictive modeling to investigate how dynamic, personalized positive experiences are encoded in the brain in humans of both sexes. Neural representations and functional integration during experiences of monetary reward, social media, music, and positive autobiographical memories were used to predict participants' affect ratings of each experience. Across experiences, positive affect was encoded in multivariate neural patterns and functional coupling of distributed cortical and subcortical brain areas, including some value-linked brain regions, like orbitofrontal cortex. Restricted sets of brain representations and functional connections linked to sensory processing were involved in encoding stimulus-specific positive affect. Our findings suggest that positive affect may be computed and communicated throughout the brain.Significance Statement Different daily life experiences can evoke positive feelings that guide our behaviors. Research on mechanisms of positive feelings often assesses rewarding experiences, like winning money, yet daily positive experiences are dynamic and multifaceted. Positive feelings linked to these experiences are subjective and varied. We built computational models that use brain activation patterns and functional connections to predict participants' positive affect ratings related to different experiences. We demonstrate that subjective positive affect is encoded in widespread patterns of neural activity and functional integration between brain regions. We show that experience-specific positive affect is sparsely encoded in activity patterns and functional connections involving sensory processing regions. Our findings show that positive feelings are encoded via complementary mechanisms throughout the brain.

The growing demand for sustainable construction materials has driven research into the use of nanosilica (nS) as an additive in concrete, due to its ability to enhance mechanical properties and reduce environmental impact. At the same time, artificial intelligence (AI) has established itself as a strategic tool for optimizing mix designs. This study offers a comparative analysis of recent advances in the incorporation of nanosilica and the application of AI models in sustainable concretes. A critical literature review was conducted on publications from 2019 to 2025, using databases such as Google Scholar and ScienceDirect, evaluating improvements in strength, durability, optimal dosing, and environmental impact. Results indicate that adding nanosilica in proportions ranging from 1.5% to 3% can increase compressive strength by up to 20% and significantly reduce chloride penetration. The study concludes that integrating nanotechnological additives, environmental assessment, and predictive algorithms provides an effective pathway for developing next-generation concrete. Additionally, it highlights the need to standardize dosing and dispersion methodologies, and to strengthen multi-objective computational models capable of simultaneously optimizing technical performance, sustainability, and economic feasibility within the industry.

Arteriovenous (AV) fistulas, the preferred vascular access for hemodialysis, fail to mature in up to 60% of patients with kidney failure. This high failure rate is often attributed to adverse hemodynamic conditions, yet the exact mechanisms remain poorly understood. This review explores the application of computational fluid dynamics (CFD) and machine learning (ML) to elucidate these mechanisms and predict clinical outcomes. CFD models have been instrumental in characterizing the complex interplay between AV fistula geometry, such as anastomotic angle and curvature, and hemodynamic parameters, such as wall shear stress (WSS) and oscillatory shear index (OSI). These studies consistently link disturbed flow patterns, including low WSS and high OSI, to regions prone to neointimal hyperplasia and stenosis. Concurrently, ML models have demonstrated significant promise in predicting AV fistula maturation, stenosis, and failure by leveraging diverse data sources, including clinical characteristics, ultrasound imaging, and acoustic bruit analysis. While powerful, the clinical utility of these computational models is often limited by small, single-center datasets, a lack of external validation, and simplifying assumptions that may not capture true physiological complexity. Future progress depends on integrating these complementary approaches, utilizing larger and more diverse datasets, and validating models prospectively to create generalizable tools that can guide surgical planning and improve AV fistula maturation rates.

Computational modelling of materials and catalysis is now indispensable in an industrial setting, fuelled by powerful algorithms and increasing computational speed. This article explores how multiscale modelling techniques are being used to accelerate product development cycles and innovate the way in which new materials are discovered. We discuss current multiscale approaches starting from the atomic simulation to the continuum methods, illustrating how these are applied to provide insight and understanding to real-world catalysts. We illustrate how understanding reactions at atomic and molecular levels directly affects large-scale industrial processes. A key focus is the essential link between model predictions and experimental validation, often requiring advanced characterization. We also address current methodological limitations and explore solutions offered by emerging techniques such as machine learning (ML). Ultimately, this article highlights the transformative role of multiscale modelling in connecting microscopic phenomena with macroscopic performance, significantly accelerating the design and development of new industrial products. This article is part of the theme issue 'Surfaces, interfaces and heterogeneous catalysis'.

Confidence-accuracy dissociations in perceptual decision making.

Information selection plays a crucial role in how individuals navigate online content. While confirmation bias has been implicated in this phenomenon, its interaction with reinforcement learning dynamics and internal confidence signals remains poorly understood. Here, we examined how veracity judgments and confidence shape choices when probabilistic rewards are tied to different epistemic attributes of news headlines. Participants completed a three-phase paradigm that combined news classification, a probabilistic learning task with varying reward contingencies, and a final reevaluation phase. Using real and false headlines judged for veracity and confidence, we created personalized sets of stimulus categories that were later used in a two-armed bandit task. In different blocks of trials, reinforcement was probabilistically associated with either the perceived truthfulness or confidence of each item. Across all experimental phases, pupil dilation provided neurophysiological signatures of belief-related processing. At a behavioral level, participants showed higher accuracies and learning rates when rewards were contingent on their previous judgments of veracity, whereas performance was markedly reduced when reinforcement favored confidence, especially low-confidence options. Pupillometric data revealed predecisional modulations tied to subjective confidence, while computational modeling showed that participants relied on feature-based generalization when veracity predicted reward and shifted toward valence-sensitive updating when contingencies no longer matched their prior epistemic structure. Together, these results reveal how veracity and confidence jointly guide reinforcement-driven choices and modulate the flexibility of belief-related decisions. By integrating cognitive, computational, and physiological data, our study provides a mechanistic understanding of how prior beliefs shape learning in complex and misinformation-rich contexts.

Nicotinic acetylcholine receptor of α7 type (α7-nAChR) is a ligand-gated ion channel composed of five identical α7 subunits. Secreted Ly6/uPAR-related protein-1 (SLURP-1) controls carcinoma progression by negative modulation of oncogenic α7-nAChR. In this study, we observed dramatic decrease of SLURP-1 plasma level in patients with metastatic melanoma. We suggested usage of recombinant analog of human SLURP-1 (rSLURP-1) to compensate this deficiency for metastatic melanoma treatment. rSLURP-1 did not affect viability of different patient-derived metastatic melanoma cells, but reduced migration of some of them. Metastatic melanoma cells of other lines were resistant to rSLURP-1. Antimigratory rSLURP-1 effect was mediated by α7-nAChR, while resistance to rSLURP-1 correlated with overexpression of human-specific CHRFAM7A gene, which encodes the α7 subunit with truncated N-terminal region (dupα7) able to form hybrid α7/dupα7-nAChR channels. Electrophysiological study in X. laevis oocytes showed that rSLURP-1 inhibits α7/dupα7-nAChR weaker than α7-nAChR. In contrast, 'Oncotag' peptide, which mimics the loop I of SLURP-1, inhibited α7/dupα7- and α7-nAChRs with similar efficiency. Oncotag suppressed metastatic melanoma cell migration independently on dupα7 expression. Computer modeling provided rationale for altered activities of rSLURP-1 and Oncotag on α7/dupα7-nAChR. TCGA database analysis revealed correlation between CHRNΑ7 and CHRFAM7A gene expression and worse survival prognosis for patients with metastatic melanoma. Thus, (1) low plasma SLURP-1 level may be a specific marker of metastatic melanoma development, (2) metastatic melanoma progression can be controlled by α7-nAChR inhibition, and (3) dupα7 overexpression is a new molecular mechanism of melanoma resistance to internal cholinergic control and new target for melanoma treatment.

Humans and other animals learn the value of candidate actions by interacting with their environment, which invariably requires the exertion of effort. Dopamine has been implicated in both effort and reward learning, but little is known about how these processes interact. In this double-blind study, healthy young adults (N = 42) were randomized to receive either high-dose sulpiride (a post-synaptic D2-receptor antagonist) or placebo. Participants then completed a novel two-armed bandit task, in which they weighed the effort costs associated with each option against their expected rewards. Overall, learning accuracy was lower on sulpiride compared to placebo. Computational modeling revealed that this was driven by the capacity of effort to significantly modulate learning rates on placebo but, critically, not on sulpiride. Simulations showed that the capacity of effort to modulate learning rates plays an adaptive role by improving performance in agents whose learning would otherwise be compromised by low motivation. Together, these data provide causal evidence that dopamine supports the relationship between effort and learning, and reveal a novel role for dopamine in shaping how humans learn from the consequences of their actions.

Complex interactions among RAS, RAF, KSR, and MEK isoforms, together with their interplay with clinically used kinase inhibitors, hinder accurate predictions of drug efficacy and the choice of optimal inhibitor combinations. Here, we combine structure-and-rule-based computational modeling with experiments to systematically study how KSR1 abundance modulates responses to RAF and MEK inhibitors (RAFi and MEKi) in PSN1 mutant KRASG12R/WT pancreatic cancer and MCF7 breast cancer WT KRAS cells. KSR1 knockdown did not substantially affect ppERK responses to Type I½ RAF inhibitor (Encorafenib) in both cell types, whereas ppERK sensitivity slightly decreased for Type II RAFi (TAK-632) in MCF7 cells, aligning with simulations. The efficacy of MEKi (Cobimetinib) slightly increased in MCF7 cells following KSR1 knockdown but slightly decreased in PSN1 cells where higher MEKi concentrations were required to suppress ERK signaling, as predicted by the model. Our computational models predict, and experiments validate that in RAS-mutant cells, two conformation-specific RAF inhibitors used in combination suppress the ERK pathway more effectively than a combination of MEK and RAF inhibitors irrespective of KSR1 levels.

Immunotherapy has seen success in treating patients with cancer, but variable responses underscore the need for effective patient stratification and therapy planning. Computational tools integrating multi-omics, imaging and machine learning have advanced, yet reliable personalized predictions remain challenging. This review analyzes the field through four converging paradigms: classical machine learning, deep learning, graph and network modeling, and mechanistic systems biology. We examine the evolution from correlational features to representation learning, relational inference, and causal simulation of tumor-immune dynamics, highlighting the shift towards multi-modal fusion and interpretable, clinically deployable models. By providing an integrated review of these computational tools, we hope to bring the community closer to achieving precision immuno-oncology for personalized cancer treatments.

Cellulose-based carbohydrates are critical precursors of hazardous C1-C3 aldehydes emitted during biomass combustion. Among these, 5,6-anhydroglucopyranose (AHGlu), a monomeric fragment of cellulose with a terminal methylene group, is expected to contribute significantly to the formation of formaldehyde, acetaldehyde, glyoxal, and methylglyoxal. This study aims to elucidate the elementary reaction pathways leading to C1-C3 aldehyde formation from AHGlu during pyrolysis by using density functional theory (DFT) and transition state theory (TST). After excluding kinetically unfavorable pathways, AHGlu is predicted to convert into straight-chain terminal-methyl carbohydrates, forming formaldehyde through a trifurcated pathway with a rate-limiting activation barrier of 205.2 kJ/mol. Acetaldehyde formation proceeds via a bifurcated pathway with a rate-limiting activation barrier of 195.9 kJ/mol, while glyoxal and methylglyoxal are generated through a single-step isomerization-scission mechanism, with rate-limiting activation barriers of 189.8 and 177.2 kJ/mol, respectively. The uncertainty in calculating energy at the basis set level and the perturbation of the pressure dependence of the reaction on the final results were evaluated. Using reaction rates at 1 atm, comparative reactor simulations deviated from experimental references by less than 20%, indicating that the proposed mechanism is applicable to cellulose pyrolysis. According to sensitivity analysis, promoting the conversion of AHGlu toward LVG prior to the ring-opening step or conducting pyrolysis at pressures below 0.1 atm may substantially suppress aldehyde formation, particularly FMAD and ACAD. The formation mechanism of C1-C3 aldehydes from AHGlu reported here provides a further solid foundation for the thermal decomposition of cellulose-based anhydroglucose, and considering the catalytic effect of water in relation to these research findings will lead to a deeper understanding.

Nickel-based powder metallurgy (PM) superalloys are indispensable for aero-engine turbine disks, but their design is constrained by complex multicomponent interactions and the difficulty in predicting long-term service performance. Herein, we present a data-driven framework integrating high-throughput thermodynamic calculations, diffusion-multiple experiments, and transfer learning to predict long-term microstructural stability and mechanical properties. By calibrating computational models with sparse experimental data, our transfer learning approach enables accurate prediction of microstructural features, which further serve as inputs for mechanical property modeling. From a screening of 105 compositions, we identify a promising low-density (8.33 g/cm3) alloy, designated USTB-PM750. This alloy exhibits a yield strength of 1138 MPa and a creep life of 141 h to 0.2% strain at 750°C under 480 MPa. Microstructural analysis reveals that its superior performance stems from a low stacking-fault energy, which promotes the formation of stacking faults and microtwins. These defects subsequently evolve into dense networks of Lomer-Cottrell locks, further reinforced by solute-segregation-induced local phase transformations. This approach significantly improves alloy design efficiency and offers a promising high-performance candidate material for turbine-disk applications in advanced aero-engines.

Abstract: Compared to classical computing implementations, reversible arithmetic adders offer a valuable platform for implementing quantum computation models in digital systems and specific applications, such as cryptography and natural language processing. Reversible logic efficiently prevents energy wastage through thermal dissipation. This study presents a comprehensive exploration introducing new carry-select adders (CSLA) based on quantum and reversible logic. Five reversible CSLA designs are proposed and compared, evaluating various criteria, including speed, quantum cost, and area, compared to previously published schemes. These comparative metrics are formulated for arbitrary n-bit size blocks. Each design type is described generically, capable of implementing carry-select adders of any size.. Introduction: one of the critical factors in designing VLSI circuits and digital systems is power consumption and energy dissipation. Energy dissipation increases due to many transistors in circuits, shrinking the feature size. In the early 1960s, Landauer introduced a novel power dissipation principle in digital circuits. He issued his theory about power dissipation based on information loss during computation in digital systems. From his perspective, designing digital and logical circuits based on conventional logic, also known as irreversible logic, leads to inevitable power dissipation. The motive is that digital circuits designed based on irreversible logic contain more input lines

The Shack-Hartmann wavefront sensor (SHWS) is a widely used non-interferometric wavefront measurement technique. However, for high-slope wavefronts, spot crosstalk and asymmetric distortion cause severe matching ambiguity and centroiding errors. This creates an inherent conflict between dynamic range and reconstruction accuracy. To address this, a graph-theoretic computational model named G-SHWS is proposed. By minimizing the global pairing cost of a bipartite graph constructed between fitted and actual spots, G-SHWS drives the fitted distribution to approximate the true distribution and maps the subaperture attribution of the fitted spots to the actual spots, achieving precise spot-subaperture matching under severe aliasing. Furthermore, incorporating a Graph Attention Network (GAT) embedded with SHWS matching topology, the model utilizes a graph structure to explicitly encode the matching relationships obtained from the matching process, and combines the spatial features and intensity morphology of spots to achieve high-precision reconstruction of strongly distorted wavefronts, effectively circumventing the inherent centroiding errors under large aberrations. Experimental results demonstrate that G-SHWS extends the measurable range of SHWS to 21 times the conventional limit while maintaining a reconstruction error of less than , and remains robust under severe spot loss. These advancements significantly enhance the SHWS's ability to measure complex aberrations, providing a reliable computational framework for large dynamic range wavefront sensing.

Neurons rely on two interdependent mechanisms - homeostasis and neuromodulation - to maintain robust and adaptable functionality. Calcium homeostasis stabilizes neuronal activity by adjusting ionic conductances, whereas neuromodulation dynamically modifies ionic properties in response to external signals carried by neuromodulators. Combining these mechanisms in conductance-based models often produces unreliable outcomes, particularly when sharp neuromodulation interferes with calcium-homeostatic tuning. This study explores how a biologically inspired neuromodulation controller can harmonize with calcium homeostasis to ensure reliable neuronal function. Using computational models of stomatogastric ganglion and dopaminergic neurons, we demonstrate that controlled neuromodulation preserves neuronal firing patterns while calcium homeostasis simultaneously maintains target intracellular calcium levels. Unlike sharp neuromodulation, the neuromodulation controller integrates activity-dependent feedback through mechanisms mimicking G-protein-coupled receptor cascades. The interaction between these controllers critically depends on the existence of an intersection in conductance space, representing a balance between target calcium levels and neuromodulated firing patterns. Maximizing neuronal degeneracy enhances the likelihood of such intersections, enabling robust modulation and compensation for channel blockades. We further show that this controller pairing extends to network-level activity, reliably modulating the rhythmic activity of central pattern generators. This study highlights the complementary roles of calcium homeostasis and neuromodulation, proposing a unified control framework for maintaining robust and adaptive neural activity under physiological and pathological conditions.

Inhibitory mechanism of thrombin (FIIa) by garlic-derived H2S: persulfidation cleavage of the Cys42-Cys58 disulfide bond triggers FIIa cascade inactivation.

Quantify the long-term mechanical durability of Abbott stylet-driven leads for left bundle branch area pacing (LBBAP) using a combination of computational modeling and fatigue bench test. Intracardiac lead curvatures were simulated in a 3D computational heart model across a range of lead implant scenarios and cardiac electro-mechanical scenarios over a complete cardiac cycle to determine the range of induced stresses. Safety factor was determined using the bending curvature and mechanical properties of the conductor. A benchtop fatigue physical test was implemented at higher stress levels, thus lower safety factors, than the computational modeling cohort. Computer simulation modeling showed that maximum curvature and safety factor (SF) were comparable for LBBA implants (0.06 ± 0.02mm-1 and 4.7 ± 1.6) vs. other traditional implant locations (0.05 ± 0.03mm-1 and 5.0 ± 0.7). Lead durability was verified through successful fatigue bench testing at max curvatures of 0.12 ± 0.01mm-1 and safety factor of 2.2 ± 0.2. These test conditions represented approximately a twofold increase in maximum curvature and a 2.1-fold reduction in safety factor relative to simulated cases, over a test duration representative of 10years of service. Abbott stylet-driven leads were demonstrated to be safe for LBBAP, even at stresses far exceeding those which may be clinically observed for 400 million cardiac cycles. No differences in long-term safety were observed between LBBAP and traditional pacing locations.

The development of effective strategies for the detoxification of organophosphorus (OP) nerve agents has evolved from the early mechanistic studies of François Terrier and collaborators, who first elucidated the exceptional nucleophilicity of α-effect species such as oximes and hydroxamates, to the modern design of supramolecular and material-based systems. Terrier's pioneering kinetic investigations and the conceptual framework established by Clifford A. Bunton and Erwin Buncel on micellar catalysis provided a foundation for understanding how medium effects and local organization modulate α-nucleophile reactivity. Building on these insights, contemporary research has expanded the chemical landscape of oxime-based reactivators through synthetic modification, computational modeling, and the development of functional scaffolds capable of efficient acetylcholinesterase (AChE) reactivation and direct OP hydrolysis. This review examines the evolution of oxime-based detoxification, with emphasis on structure-reactivity relationships, mechanistic insights, and advances in reaction media. Micellar systems were the first colloidal environments explored, while supramolecular assemblies such as lipids and cyclodextrins combine molecular recognition with catalytic function. Recent developments include inorganic and nanostructured catalysts that enable organophosphate degradation under mild conditions. The transition from α-nucleophile chemistry to multifunctional materials reflects not only the progress of physical organic chemistry in detoxification but also its convergence with supramolecular and materials science.