Biophysical Model Research Articles

BDNF Differentially Affects Low- and High-Frequency Neurons in a Primary Nucleus of the Chicken Auditory Brainstem.

Neurotrophins are proteins that mediate neuronal development using spatiotemporal signaling gradients. The chicken nucleus magnocellularis (NM), an analogous structure to the mammalian anteroventral cochlear nucleus, provides a model system in which signaling between the brain-derived neurotrophic factor (BDNF) and tyrosine receptor kinase B (TrkB) is temporally regulated. In the NM, TrkB expression is high early in development (embryonic [E] day 9) and is downregulated until maturity (E18-21). It is currently unknown how BDNF-TrkB signaling affects neuronal properties throughout development and across a spatial (i.e., frequency) axis. To investigate this, we exogenously applied BDNF onto NM neurons ex vivo and studied intrinsic properties using whole-cell patch clamp electrophysiology. Early in development (E13), when TrkB expression is detectable with immunohistochemistry, BDNF application slowed the firing of high-frequency NM neurons, resembling an immature phenotype. Current measurements and biophysical modeling revealed that this was mediated by a decreased conductance of the voltage-dependent potassium channels. Interestingly, this effect was seen only in high-frequency neurons and not in low-frequency neurons. BDNF-TrkB signaling induced minimal changes in late-developing NM neurons (E20-21) of high and low frequencies. Our results indicate that normal developmental downregulation of BDNF-TrkB signaling promotes neuronal maturation tonotopically in the auditory brainstem, encouraging the appropriate development of neuronal properties.

Radiation adaptive response for constant dose-rate irradiation in high background radiation areas

AbstractThe presented paper describes the problem of human health in regions with high level of natural ionizing radiation in various places in the world. The radiation adaptive response biophysical model was presented and calibrated for the special case of constant dose-rate irradiation. The calibration was performed for the data of residents of several high background radiation areas, like Ramsar in Iran, Kerala in India or Yangjiang in China. Studied end-points were: chromosomal aberrations, cancer incidence and cancer mortality. For the case of aberrations, among collected publications about 45% have shown the existence of adaptive response. Average reduction of chromosomal aberrations was ∼ 10%, while for the case of cancer incidence it was ∼ 15% and ∼ 17% for cancer mortality (each taking into account only results showing adaptive response). Results of the other 55% of data regarding chromosomal aberrations have been tested with the LNT (linear no-threshold) hypothesis, but results were inconsistent with the linear model. The conditions for adaptive response occurrence are still unknown, but it is postulated to correlate with the distribution of individual radiosensitivity among members of surveyed populations.

Confinement Modulates Axial Patterning in Regenerating Hydra

The establishment of the body plan is a major step in animal morphogenesis. The role of mechanical forces and feedback in patterning the body plan remains unclear. Here we explore this question by studying regenerating Hydra tissues confined in narrow cylindrical channels, which constrain their morphology. We find that frustration between the orientation of the channel and the inherited axis in the regenerating tissues can lead to the formation of a multiaxial body plan. The morphological outcome is directly related to the pattern of nematic topological defects that emerges in the organization of the supracellular actomyosin fibers. When the inherited axis, which can be read out from the initial alignment of the supracellular fibers in the confined spheroid, is parallel to the channel's axis, the tissue regenerates normally into animals with a single body axis aligned with the channel. However, regenerating spheroids that are confined in a frustrated perpendicular configuration often develop excess defects (including negatively charged −1/2 defects) and regenerate into multiaxial morphologies. The influence of mechanical constraints on the regenerated body plan argues against an axial patterning mechanism that is based solely on inherited gradients of biochemical morphogens. We further show that the dependence of the regeneration outcomes on the initial tissue orientation can be recapitulated by a biophysical model, which considers the coupled dynamics of the nematic organization of the actomyosin fibers and a morphogen concentration field, incorporating a mechanochemical feedback loop involving strain-dependent morphogen production at defect sites. Published by the American Physical Society 2024

Comparing SMILES and SELFIES tokenization for enhanced chemical language modeling

Life sciences research and experimentation are resource-intensive, requiring extensive trials and considerable time. Often, experiments do not achieve their intended objectives, but progress is made through trial and error, eventually leading to breakthroughs. Machine learning is transforming this traditional approach, providing methods to expedite processes and accelerate discoveries. Deep Learning is becoming increasingly prominent in chemistry, with Convolutional Graph Networks (CGN) being a key focus, though other approaches also show significant potential. This research explores the application of Natural Language Processing (NLP) to evaluate the effectiveness of chemical language representations, specifically SMILES and SELFIES, using tokenization methods such as Byte Pair Encoding (BPE) and a novel approach developed in this study, Atom Pair Encoding (APE), in BERT-based models. The primary objective is to assess how these tokenization techniques influence the performance of chemical language models in biophysics and physiology classification tasks. The findings reveal that APE, particularly when used with SMILES representations, significantly outperforms BPE by preserving the integrity and contextual relationships among chemical elements, thereby enhancing classification accuracy. Performance was evaluated in downstream classification tasks using three distinct datasets for HIV, toxicology, and blood–brain barrier penetration, with ROC-AUC serving as the evaluation metric. This study highlights the critical role of tokenization in processing chemical language and suggests that refining these techniques could lead to significant advancements in drug discovery and material science.

Larval dispersal and physical connectivity of Pheronema carpenteri populations in the Azores

The study of larval dispersal and connectivity between deep-sea populations is essential for the effective conservation and management of deep-sea environments and the design and implementation of Marine Protected Areas. Dense sponge aggregations, known as “sponge grounds”, are a key component of marine benthic ecosystems, by increasing the structural complexity of the sea floor and providing structure and habitat for many other species. These aggregations are characteristic of the Azores deep-sea environment. These sessile organisms rely primarily on larval dispersal for their reproduction. Connectivity between specific Pheronema carpenteri sponge aggregations in the Azores was studied using a 3-D biophysical dispersal model. Different biological trait scenarios were analyzed, considering spawning seasonality and pelagic larval duration. Model results indicate that regional circulation patterns drive larval dispersion, shaping population connectivity of P. carpenteri sponge aggregations in the Azores, particularly among aggregations in the Central Group of Azorean islands. Some areas present high retention rates, receiving larvae from several sponge aggregations while also being important larval source aggregations. In contrast, aggregations from the Eastern Group may be isolated from the others. Larval dispersal and connectivity patterns were analyzed concerning the current configuration of Marine Protected Areas (MPAs) in the Azores. The results underscored the importance of maintaining protection efforts in existing MPAs and identified stepping-stone locations and specific sites where additional measures could enhance species connectivity in the Azores.

Computationally Driven Discovery and Characterization of SIRT3-Activating Compounds that Fully Recover Catalytic Activity under NAD+ Depletion

Many chronic, age-related disorders could be mitigated by enhancing the activity of enzymes that regulate biochemical signaling pathways, but all known modes of enzyme activation rely on allosteric binding sites, which have only been identified in less than 10% of proteins. Sirtuins (SIRT1-7) are nicotinamide adenine dinucleotide (NAD+)-dependent deacylases playing critical roles in lifespan and age-related diseases. The physiological importance of sirtuins and the reduction in their catalytic activity with the age-related decline in NAD+ levels have stimulated intense interest in designing sirtuin-activating compounds; however, except for substrate-specific allosteric SIRT1 activators, methodologies for rational design of sirtuin-activating compounds are lacking. Here, we introduce methods for the activation of such enzymes that do not rely on allosteric binding sites, and we demonstrate their successful application to the discovery of first-in-class activators of sirtuin enzymes. We establish how all-atom simulations of an enzyme’s active site under the potential of a small molecule modulator can be used to identify molecular properties that achieve desired changes in local enzyme conformational degrees of freedom conducive to the enhancement of catalytic activity. We apply computational high-throughput screening based on this biophysical model for activation of sirtuin enzymes to the major mitochondrial sirtuin SIRT3, which plays a critical role in age-related disorders but does not have a known allosteric site; we thereby identify first-in-class, nonallosteric activators of this enzyme. These compounds are the first reported steady-state activators of the major mitochondrial sirtuin enzyme, and they operate according to a mode of action not shared by any existing drug. Two such compounds can almost double the catalytic efficiency of SIRT3 with respect to NAD+, thus compensating for the loss in SIRT3 activity that occurs due to the age-related decline in NAD+, and they may be developed for therapeutic applications to combat multiple types of age-related disorders. These discoveries establish a foundation for the development of a new class of drugs that function through the activation of enzymes by modulation of local conformational ensembles. Published by the American Physical Society 2024

Applying Spatiotemporal Modeling of Cell Dynamics to Accelerate Drug Development.

Cells act as physical computational programs that utilize input signals to orchestrate molecule-level protein-protein interactions (PPIs), generating and responding to forces, ultimately shaping all of the physiological and pathophysiological behaviors. Genome editing and molecule drugs targeting PPIs hold great promise for the treatments of diseases. Linking genes and molecular drugs with protein-performed cellular behaviors is a key yet challenging issue due to the wide range of spatial and temporal scales involved. Building predictive spatiotemporal modeling systems that can describe the dynamic behaviors of cells intervened by genome editing and molecular drugs at the intersection of biology, chemistry, physics, and computer science will greatly accelerate pharmaceutical advances. Here, we review the mechanical roles of cytoskeletal proteins in orchestrating cellular behaviors alongside significant advancements in biophysical modeling while also addressing the limitations in these models. Then, by integrating generative artificial intelligence (AI) with spatiotemporal multiscale biophysical modeling, we propose a computational pipeline for developing virtual cells, which can simulate and evaluate the therapeutic effects of drugs and genome editing technologies on various cell dynamic behaviors and could have broad biomedical applications. Such virtual cell modeling systems might revolutionize modern biomedical engineering by moving most of the painstaking wet-laboratory effort to computer simulations, substantially saving time and alleviating the financial burden for pharmaceutical industries.

Evaluating Models of the Ageing BOLD Response.

Neural activity cannot be directly observed using fMRI; rather it must be inferred from the hemodynamic responses that neural activity causes. Solving this inverse problem is made possible through the use of forward models, which generate predicted hemodynamic responses given hypothesised underlying neural activity. Commonly-used hemodynamic models were developed to explain data from healthy young participants; however, studies of ageing and dementia are increasingly shifting the focus toward elderly populations. We evaluated the validity of a range of hemodynamic models across the healthy adult lifespan: from basis sets for the linear convolution models commonly used to analyse fMRI studies, to more advanced models including nonlinear fitting of a parameterised hemodynamic response function (HRF) and nonlinear fitting of a biophysical generative model (hemodynamic modelling, HDM). Using an exceptionally large sample of participants, and a sensorimotor task optimized for detecting the shape of the BOLD response to brief stimulation, we first characterised the effects of age on descriptive features of the response (e.g., peak amplitude and latency). We then compared these to features from more complex nonlinear models, fit to four regions of interest engaged by the task, namely left auditory cortex, bilateral visual cortex, left (contralateral) motor cortex and right (ipsilateral) motor cortex. Finally, we validated the extent to which parameter estimates from these models have predictive validity, in terms of how well they predict age in cross-validated multiple regression. We conclude that age-related differences in the BOLD response can be captured effectively by models with three free parameters. Furthermore, we show that biophysical models like the HDM have predictive validity comparable to more common models, while additionally providing insights into underlying mechanisms, which go beyond descriptive features like peak amplitude or latency, and include estimation of nonlinear effects. Here, the HDM revealed that most of the effects of age on the BOLD response could be explained by an increased rate of vasoactive signal decay and decreased transit rate of blood, rather than changes in neural activity per se. However, in the absence of other types of neural/hemodynamic data, unique interpretation of HDM parameters is difficult from fMRI data alone, and some brain regions in some tasks (e.g., ipsilateral motor cortex) can show responses that are more difficult to capture using current models.

Predicting the macrovascular contribution to resting-state fMRI functional connectivity at 3 Tesla: A model-informed approach

Abstract Macrovascular biases have been a long-standing challenge for functional magnetic resonance imaging (fMRI), limiting its ability to detect spatially specific neural activity. Recent experimental studies, including our own, found substantial resting-state macrovascular blood-oxygenation level-dependent (BOLD) fMRI contributions from large veins and arteries, extending into the perivascular tissue at 3 T and 7 T. The objective of this study is to demonstrate the feasibility of predicting, using a biophysical model, the experimental resting-state BOLD fluctuation amplitude (RSFA) and associated functional connectivity (FC) values at 3 Tesla. We investigated the feasibility of both 2D and 3D infinite-cylinder Models as well as macrovascular anatomical networks (macro-VANs) derived from angiograms. Our results demonstrate that (1) with the availability of macro-VANs, it is feasible to model macrovascular BOLD FC using both the macro-VAN-based model and 3D infinite-cylinder Models, though the former performed better; (2) biophysical modelling can accurately predict the BOLD pair-wise correlation near to large veins (with R2 ranging from 0.53 to 0.93 across different subjects), but not near to large arteries; (3) compared with FC, biophysical modelling provided less accurate predictions for RSFA; (4) modelling of perivascular BOLD connectivity was feasible at close distances from veins (with R2 ranging from 0.08 to 0.57), but not arteries, with performance deteriorating with increasing distance. While our current study demonstrates the feasibility of simulating macrovascular BOLD in the resting state, our methodology may also apply to understanding task-based BOLD. Furthermore, these results suggest the possibility of correcting for macrovascular bias in resting-state fMRI and other types of fMRI using biophysical modelling based on vascular anatomy.

Inference and design of antibody specificity: From experiments to models and back.

Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Experimental methods for generating specific binders rely on in vitro selection, which is limited in terms of library size and control over specificity profiles. Additional control was recently demonstrated through high-throughput sequencing and downstream computational analysis. Here we follow such an approach to demonstrate the design of specific antibodies beyond those probed experimentally. We do so in a context where very similar epitopes need to be discriminated, and where these epitopes cannot be experimentally dissociated from other epitopes present in the selection. Our approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies.

Dynamic barriers modulate cohesin positioning and genome folding at fixed occupancy.

In mammalian interphase cells, genomes are folded by cohesin loop extrusion limited by directional CTCF barriers. This interplay leads to the enrichment of cohesin at barriers, isolation between neighboring topologically associating domains, and elevated contact frequency between convergent CTCF barriers across the genome. However, recent in vivo measurements present a puzzle: reported residence times for CTCF on chromatin are in the range of a few minutes, while lifetimes for cohesin are much longer. Can the observed features of genome folding result from the action of relatively transient barriers? To address this question, we developed a dynamic barrier model, where CTCF sites switch between bound and unbound states with rates that can be directly compared with biophysical measurements. Using this model, we investigated how barrier dynamics would impact observables for a range of experimental genomic and imaging datasets, including ChIP-seq, Hi-C, and microscopy. We found the interplay of CTCF and cohesin binding timescales influence the strength of each of these features, leaving a signature of barrier dynamics even in the population-averaged snapshots offered by genomic datasets. First, in addition to barrier occupancy, barrier bound times are crucial for instructing features of genome folding. Second, the ratio of boundary to extruder lifetime greatly alters simulated ChIP-seq and simulated Hi-C. Third, large-scale changes in chromosome morphology observed experimentally after increasing extruder lifetime require dynamic barriers. By integrating multiple sources of experimental data, our biophysical model argues that CTCF barrier bound times effectively approach those of cohesin extruder lifetimes. Together, we demonstrate how models that are informed by biophysically measured protein dynamics broaden our understanding of genome folding.

Thermostatted Kinetic Theory Structures in Biophysics: Generalizations and Perspectives

The mathematical modeling of multicellular systems is an important branch of biophysics, which focuses on how the system properties emerge from the elementary interaction between the constituent elements. Recently, mathematical structures have been proposed within the thermostatted kinetic theory for the modeling of complex living systems and have been profitably employed for the modeling of various complex biological systems at the cellular scale. This paper deals with a class of generalized thermostatted kinetic theory frameworks that can stand in as background paradigms for the derivation of specific models in biophysics. Specifically, the fundamental homogeneous thermostatted kinetic theory structures of the recent literature are recovered and generalized in order to take into consideration further phenomena in biology. The generalizations concern the conservative, the nonconservative, and the mutative interactions between the inner system and the outer environment. In order to sustain the strength of the new structures, some specific models of the literature are reset into the style of the new frameworks of the thermostatted kinetic theory. The selected models deal with breast cancer, genetic mutations, immune system response, and skin fibrosis. Future research directions from the theoretical and modeling viewpoints are discussed in the whole paper and are mainly devoted to the well-posedness in the Hadamard sense of the related initial boundary value problems, to the spatial–velocity dynamics and to the derivation of macroscopic-scale dynamics.

Salt Supersaturation as an Accelerator of Influenza A Virus Inactivation in 1 μL Droplets.

Influenza A virus (IAV) spreads through exhaled aerosol particles and larger droplets. Estimating the stability of IAV is challenging and depends on factors such as the respiratory matrix and drying kinetics. Here, we combine kinetic experiments on millimeter-sized saline droplets with a biophysical aerosol model to quantify the impact of NaCl on IAV stability. We show that IAV inactivation is determined by NaCl concentration, which increases during water evaporation and then decreases again when efflorescence occurs. When drying in air with relative humidity RH = 30%, inactivation follows an inverted sigmoidal curve, with inactivation occurring most rapidly when the NaCl concentration exceeds 20 mol/(kg H2O) immediately prior to efflorescence. Efflorescence reduces the NaCl molality to saturated conditions, resulting in a significantly reduced inactivation rate. We demonstrate that the inactivation rate k depends exponentially on NaCl molality, and after the solution reaches equilibrium, the inactivation proceeds at a first-order rate. Introducing sucrose, an organic cosolute, attenuates IAV inactivation via two mechanisms: first by decreasing the NaCl molality during the drying phase and second by a protective effect against the NaCl-induced inactivation. For both pure saline and sucrose-containing droplets, our biophysical model ResAM accurately simulates the inactivation when NaCl molality is used as the only inactivating factor. This study highlights the role of NaCl molality in IAV inactivation and provides a mechanistic basis for the observed inactivation rates.

Toward understanding the hydrologic, ecologic and community flooding implications of coastal restoration strategies: Sediment diversions

Evaluating the real-world impacts of proposed restoration strategies is a complex process. Typically, restoration is pursued to achieve a number of primary and secondary objectives as most coastal and deltaic areas support a variety of functions and activities with substantial social and economic values. In this analysis, we demonstrate the importance of considering the broad implications of planning and implementing restoration projects. We use a recently developed simplified and computationally efficient biophysical numerical model. The Mid-Barataria Sediment Diversion, a restoration project approximately 60 km down the Mississippi River from New Orleans, Louisiana, USA, offers an opportunity to quantify the benefits and potential impacts of large-scale restoration. Operation plans of the diversion are employed in this analysis to reveal the delicate balance between its potential benefits and possible adverse effects. Evaluation metrics include net land change and landscape evolution, change in salinity and subsequent shift in the distribution of marsh types, long-term change in the hydroperiod and marsh inundation, and change in the flood risk for unique local coastal communities and culture. We also consider subaqueous basin infilling, spatial sediment deposition, and creation of shallow water areas that might be of ecological value. These metrics are evaluated collectively under a set of future scenarios capturing long-term impacts of sea level rise and subsidence. This analysis provides evidence in favor of using a multi-metric approach to holistically evaluate restoration during the planning and design phases, as well as to guide the process of adaptively managing restoration projects post construction.

Modulation of SpyCatcher Ligation Kinetics by SpyTag Thioamide Substitution.

Thioamide substitutions have been shown to impart valuable properties on peptides for biophysical experiments as well as cell or in vivo studies, but a rational understanding of thioamide effects on protein structure and protein-protein interactions is lacking. To elucidate their effects in β-sheet structures, we have used SpyCatcher003-SpyTag003 as a host-guest system to study individual thioamide incorporation at eight different positions in the SpyTag peptide. We have demonstrated that incorporating thioamides into SpyTag at specific positions can result in a ∼2-fold faster ligating complex, as well as >2000-fold slower ligating complex. Biophysical analysis and structural modeling provide a reasonable explanation for most of the thioamide effects, altering hydrogen bond networks as well as modulating an n→π* interaction within the SpyTag peptide. Our findings have important implications for potential applications of thioamide SpyTag variants, where the thioamide could impart protease stability in cells while also controlling the rate of ligation to SpyCatcher. These SpyCatcher-SpyTag host-guest experiments will also help to build a database for predicting thioamide effects on protein structure and function.

Assessing tumor microstructure with time-dependent diffusion imaging: Considerations and feasibility on clinical MRI and MRI-Linac.

Quantitative imaging biomarkers (QIBs) can characterize tumor heterogeneity and provide information for biological guidance in radiotherapy (RT). Time-dependent diffusion MRI (TDD-MRI) derived parameters are promising QIBs, as they describe tissue microstructure with more specificity than traditional diffusion-weighted MRI (DW-MRI). Specifically, TDD-MRI can provide information about both restricted diffusion and diffusional exchange, which are the two time-dependent effects affecting diffusion in tissue, and relevant in tumors. However, exhaustive modeling of both effects can require long acquisitions and complex model fitting. Furthermore, several introduced TDD-MRI measurements can require high gradient strengths and/or complex gradient waveforms that are possibly not available in RT settings. In this study, we investigated the feasibility of a simple analysis framework for the detection of restricted diffusion and diffusional exchange effects in the TDD-MRI signal. To promote the clinical applicability, we use standard gradient waveforms on a conventional 1.5 T MRI system with moderate gradient strength (Gmax=45 mT/m), and on a hybrid 1.5 T MRI-Linac system with low gradient strength (Gmax=15 mT/m). Restricted diffusion and diffusional exchange were simulated in geometries mimicking tumor microstructure to investigate the DW-MRI signal behavior and to determine optimal experimental parameters. TDD-MRI was implemented using pulsed field gradient spin echo with the optimized parameters on a conventional MRI system and a MRI-Linac. Experiments in green asparagus and 10 patients with brain lesions were performed to evaluate the time-dependent diffusion (TDD) contrast in the source DW-images. Simulations demonstrated how the TDD contrast was able to differentiate only dominating diffusional exchange in smaller cells from dominating restricted diffusion in larger cells. The maximal TDD contrast in simulations with typical cancer cell sizes and in asparagus measurements exceeded 5% on the conventional MRI but remained below 5% on the MRI-Linac. In particular, the simulated TDD contrast in typical cancer cell sizes (r=5-10µm) remained below or around 2% with the MRI-Linac gradient strength. In patients measured with the conventional MRI, we found sub-regions reflecting either dominating restricted diffusion or dominating diffusional exchange in and around brain lesions compared to the noisy appearing white matter. On the conventional MRI system, the TDD contrast maps showed consistent tumor sub-regions indicating different dominating TDD effects, potentially providing information on the spatial tumor heterogeneity. On the MRI-Linac, the available TDD contrast measured in asparagus showed the same trends as with the conventional MRI but remained close to typical measurement noise levels when simulated in common cancer cell sizes. On conventional MRI systems with moderate gradient strengths, the TDD contrast could potentially be used as a tool to identify which time-dependent effects to include when choosing a biophysical model for more specific tumor characterization.

Entropy generation minimization and activation energy in magneto-thermo-solutal stratified Ellis hybrid nanofluid flow through a stretching stenotic artery: Insights into cardiovascular disease

This article explores the impact of binary chemical reactions with activation energy on the flow of magnetized mono and hybrid nanofluids within a porous artery under the effects of viscous dissipation, nonlinear thermal radiation, and Joule heating. The main novelty of this study lies in considering a non-Newtonian Ellis fluid model of blood through a stretching stenotic artery consisting of gold (Au) and silver (Ag) nanoparticles. Additionally, velocity slip, convective temperature, and concentration boundary conditions are applied at the arterial surface. The proposed model compares the performance of Ag/Blood nanofluid and Au-Ag/Blood hybrid nanofluid models. The governing equations are solved using the bvp4c built-in solver in MATLAB, which employs advanced numerical techniques and algorithms tailored for BVPs, ensuring quicker convergence with minor errors and accurate solutions. Graphs are plotted for concentration, temperature, velocity, entropy minimization, skin friction coefficient, Nusselt number, and Sherwood number for various physical parameters. The results show that with increased nonlinear thermal radiation (0.1≤Nr≤2.0), thermal Biot number (0.1≤Bi1≤0.3), and Eckert number (0.01≤Ec≤0.2), the thermal distribution profile exhibits a rising trend. In contrast, with increasing fluctuations in the chemical reaction rate parameter (0.1≤Rc≤0.3) and Schmidt number (1.0≤Sc≤3.0), the concentration distribution profile exhibits a decreasing trend. In terms of mass and heat transfer efficiency, the Au-Ag/Blood hybrid nanofluid flow outperforms the Ag/Blood nanofluid model. More entropy is observed for Au-Ag/Blood compared to Ag/Blood in the stenotic artery. Heat energy may be created by targeting the injured area and avoiding harm to nearby tissues. Localized heating can widen arteries and increase blood flow to the affected locations, potentially aiding cardiovascular health management and personalized therapy. This study has various implications for the design of drug delivery systems, biophysical models, innovative materials, and our knowledge of fluid dynamics and heat transfer. However, nanofluid/hybrid nanofluid stability is challenging for current researchers since this modeled flow can be considered over a shrinking artery or in the trihybrid model in future studies.

Radiation exposure of astronauts following an intense solar particle event: analysis and comparison of doses in male and female voxel phantoms.

According to NASA's plans, a human travel to the Moon is planned by the end of 2025 with the Artemis II mission, and humans should land on the Moon again in 2026. Exposure to space radiation is one of the main risks for the crew members; while for these short missions the doses from galactic cosmic rays would be relatively low, the possible occurrence of an intense solar particle event (SPE) represents a major concern, especially considering that in 2025 the Sun activity will be at its peak. Quantifying the amount and the effects of such exposure is therefore crucial, to identify shielding conditions that allow respecting the dose limits established by the various space agencies. By exploiting an interface between the BIANCA biophysical model and the FLUKA Monte Carlo radiation transport code, in this work we implemented a male and a female voxel phantom and we calculated absorbed doses and Gy-Eq doses in the various tissues/organs, as well as effective doses, following exposure to the August 1972 SPE, the most intense event of the modern era. The calculations were performed respect the organ dose limits for 30 d missions. A detailed comparison between male and female doses was then carried out, also considering that the Artemis II crew will include a woman. The results showed that female doses tend to be higher than male doses, especially with light shielding. This should be taken into account in mission design, also considering that, in a typical lunar mission, up to 15% of time may be spent in extra-vehicular activities, and thus with light shielding. More generally, this work outlines the importance of performing separate calculations for male and female astronauts when dealing with radiation doses and effects.

Spatiotemporal dynamics of nucleocytoplasmic transport

Nucleocytoplasmic transport is essential for cellular function, presenting a canonical example of rapid molecular sorting inside cells. It consists of a coordinated interplay between import/export of molecules in/out the cell nucleus. Here, we investigate the role of spatiotemporal dynamics of the nucleocytoplasmic transport and its regulation. We develop a biophysical model that captures the main features of the nucleocytoplasmic transport, in particular, its regulation through the Ran cycle. Our model yields steady-state profiles for the molecular components of the Ran cycle, their relaxation times, as well as the nuclear-to-cytoplasmic molecule ratio. We show that these quantities are affected by their spatial dynamics and heterogeneity within the nucleus. Specifically, we find that the spatial nonuniformity of Ran guanine exchange factor (RanGEF)—particularly its proximity to the nuclear envelope—increases the Ran content in the nucleus. We further show that RanGEF's accumulation near the nuclear envelope results from its intrinsic dynamics as a nuclear cargo, transported by the Ran cycle itself. Overall, our work highlights the critical role of molecular spatial dynamics in cellular processes and proposes new avenues for theoretical and experimental inquiries into the nucleocytoplasmic transport. Published by the American Physical Society 2024

Bifurcation enhances temporal information encoding in the olfactory periphery.

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.