Decoding the social mind in depression: A computational dissociation between explicit trust and implicit belief updating.

Protein design enables the creation of novel structures and functions beyond those found in nature, with recent progress accelerated by computational modeling and machine learning. However, many automated methods act as black boxes, limiting mechanistic insight. Here we highlight the continuing importance of rational protein design, defined as an approach rooted in physical principles, chemical intuition, and sequence-structure-function relationships. We outline three complementary strategies: backbone-first, sequence-first, and function-first, which provide interpretable design frameworks and enable robust scaffold generation, motif incorporation, and functional engineering. Looking forward, we argue that hybrid workflows combining rational principles with machine learning offer the most promising route to dynamic, explainable, and generalizable protein design.

Acid-sensing ion channels (ASICs) are members of the DEG/ENaC family that includes the only known peptide-gated ion channels. While ASICs are gated by protons, they are also sensitive to peptides and are modulated by the molluscan FMRFamide and other mammalian neuropeptides ending by the RFamide motif. We identified a set of synthetic short amidated hexapeptides, which not only end by the RFamide motif but also by CFamide and FCamide, as potent positive modulators of ASIC3 acid-induced activity. We focused on two of them, a RFamide peptide (FRCC‾RFamide) and a CFamide peptide (FRCRC‾Famide), demonstrating that they have similar specificity for and effects on ASIC3. The potentiating effects of the two peptides are due to a strong slow-down of desensitization, leading to an increase in the amount of current induced by acid pH (≤pH6.6), with apparent affinities ranging from 1 to 5μM. Surprisingly, the washout kinetic of FRCC‾RFamide peptide was much slower than those of FRCRC‾Famide and other known RFamide peptides, suggesting potential differences in their mechanisms of action. Computational modeling and structure-function analysis reveal interactions of both peptides with the non-proton binding site of ASIC3 as already reported before for other RFamide peptides, but our data also suggest possible additional effects of FRCC‾RFamide involving directly or indirectly the proton binding domain. These findings expand our understanding of ASICs' modulation by peptides, identifying novel short modulators of ASIC3, including peptides with new CFamide and FCamide ending motifs, and showing differences between these peptides using their washout kinetic as a new parameter.

Carrier effects of microplastics in a hydroponic system: Amplifying diethyl phthalate toxicity and endophytic dysbiosis in Rye (Secale cereale L.) with implications for aquatic ecosystems.

Understanding surface charging limitations of hematite photoanodes through combining cathodic discharge measurements and computational modeling

Computational modeling investigates the neural mechanisms underlying working memory updating impairments in depression.

Integrated study on gasification of bio-slurry: Experimental validation and computational modeling

From permeability to prediction: evolving strategies for evaluating oral drug absorption.

Inferring the structure of pedestrian flows at a transportation hub.

Computational modelling in fuel cell research: From density functional theory to computational fluid dynamics

Machine learning assisted raman spectroscopy for the classification of ovarian cancer cells.

Understanding complaint behavior in mobile banking: A psychological and AI-based analysis of emotional drivers.

While substantial knowledge exists about the way the retina processes simple stimuli, our understanding of how the retina processes natural stimuli remains limited. Here we highlight key challenges that remain to be addressed to understand retinal processing of natural stimuli and describe emerging research avenues to overcome them. A key issue is model complexity. When complexifying the probing stimuli towards natural stimuli, the number of parameters required in models of retinal computations increases, raising issues of overfitting, generalization, and interpretability. This increase in complexity also poses a challenge for normative approaches, as it makes it difficult to derive non-linear retinal computations from simple principles. We describe two approaches that may help circumvent this issue. First, we propose that a new form of reductionism is emerging: instead of breaking down natural stimuli into sums of simpler stimuli, it becomes possible to "divide and conquer" natural scenes into different visual inputs corresponding to different visual tasks, allowing to study retinal computations separately for each of these tasks. Moreover, the abstract computations performed by some cell types may be understood as the result of being constrained by multiple tasks. Second, several studies suggest that it will soon be possible to mitigate the issue of complexity, by "embodying" models with more biological constraints, in particular those derived from connectomic studies. Together, these approaches offer a powerful strategy to tackle current limitations and advance our understanding of how the retina processes natural visual environments, and suggest methods that could be used in other sensory areas.

A partially-algebraic two-fluid model for efficient computation of solid–liquid two-phase flows with fine particles in hydraulic machinery

Hemodynamic instability is a highly prevalent, complex and life-threatening condition in critically ill patients. Its multifactorial nature and patient-specific variability challenge standardised treatment approaches. Computational physiological models (CPMs) offer a promising solution by simulating cardiovascular dynamics to guide individualised hemodynamic management. This systematic review evaluates the current landscape of cardiovascular CPMs, focusing on their design, credibility, and clinical readiness. A systematic search was conducted in MEDLINE ALL, Embase, Scopus, and Web of Science. Original research articles describing zero-dimensional, closed-loop cardiovascular models with (potential) applications in critical care were included. Data were extracted on context of use, model design, and validation. Model credibility was assessed using a risk-based framework and clinical readiness using a nine-level technology maturity scale. Out of 10,704 screened articles, 183 were included. Direct clinical applications were described in 50% of these studies, including diagnosis, decision support, and closed-loop control. Fluid management was the most common application domain (30%). Personalisation of model parameters was reported in 25% of the articles. While 66% of the articles presented model validation, only 21% achieved moderate credibility scores. Reporting of model characteristics was consistently (100%) insufficient. Most models (75%) were at clinical readiness level 3-4 (model prototyping and development), with four studies reaching clinical testing (level 6-8). A substantial body of cardiovascular CPMs exists with promising prospects for relevant applications in critical care, while a large part is currently confined to pre-clinical research settings. Advancing clinical integration requires leveraging existing models, improving transparency in verification and validation, and establishing robust personalisation strategies. PROSPERO - CRD42022300137, registered on February 11, 2022.

Reliable sample temperature measurements are essential in environmental transmission electron microscope (ETEM) experiments. In this study, the effect of flowing gases on the temperature distribution at a MEMS microheater in gas phase is investigated. A computational fluid dynamics model is developed and compared with experimental data. The modeling results agree well with experimental measurements based on the melting temperature of Zn nanoparticles, confirming the model's reliability. The results show that the temperature profile across the heating chip in the case with H2 environment is less uniform compared to the case of vacuum and O2. For example, at a set temperature of 900 °C in 3 mbar H2, a temperature difference of 60 °C is observed between the central sample position compared to the surrounding arc-shaped heater which also is the temperature sensor, while the difference in the vacuum case is only 13 °C. Temperature is one of the key parameters in in-situ TEM experiments and, therefore, these findings are important in the design of ETEM experiments, especially when using MEMS microheaters with relatively large distances between TEM sample and microheater/sensor.

KMGTMDA: KAN-driven multiscale graph neural network and context-enhanced prediction for human microbe-disease associations.

String cavitation in injector nozzles has been shown to improve spray atomization while mitigating cavitation erosion. Herein, to comprehensively investigate flow behaviors under realistic engine conditions, we employ a validated computational fluid dynamics model to simulate coupled in-nozzle wall-bounded cavitation flow and near-nozzle spray flow. The characteristics of multiphase flow are extracted using the Omega vortex identification method and Lamb vector analysis. Under nine sets of representative operating conditions, injection performance is assessed using flow coefficient, total pressure recovery coefficient, vapor phase distribution, and kinetic energy transport equation. Near-nozzle spray behavior is evaluated based on the momentum flux, liquid breakup rate, and vorticity transport equation. The results indicate that the high-disturbance nozzle has good application prospects and exhibits a flow coefficient of 0.7–0.8, whereas the total pressure recovery coefficient varies between 0.6 and 0.7. Optimal injection performance is observed under conventional conditions, whereas atomization quality is enhanced under idle conditions. Additionally, cylinder pressure causes jet momentum attenuation under accelerating conditions and considerably affects the development of string cavitation in the jet core under idle and normal conditions, forming bidirectional tearing and one-way tearing of liquid film with different vorticity transport terms, thus affecting the atomization process.

Automated hemodynamic modeling to explore arterial curvature effects on intracranial aneurysm initiation.

An enhanced MQRBF-FD method with parallel computing and multiscale modeling for efficient elastic wave propagation