Realistic Patterns Research Articles

Unveiling realistic mobility patterns with home–origin–destination data aggregation

The availability of increasingly abundant mobility data in recent years has opened up new avenues for researchers to unravel human mobility patterns. Data aggregation methods have been introduced to gain a quantitative understanding of collective individual movements using these data. Nevertheless, the widely adopted origin–destination (OD) aggregation method for human mobility data lacks an essential piece of information: home location, which plays a vital role in characterizing individual movement patterns. In this study, we propose a novel data aggregation approach called home–origin–destination (HOD) with the aim of improving the accuracy of human mobility estimation. We compare the performance of various data aggregation methods for estimating population distribution. Our experimental results reveal more realistic mobility patterns when incorporating estimated home information, where individuals move out in the morning and return home before midnight. To further evaluate the effectiveness of the HOD approach, we conduct an entropy analysis to measure the unpredictability of human mobility. The HOD results exhibit lower entropy values than those in the other two cases, OD and home–destination (HD). These findings underscore the importance of incorporating home information in understanding and modeling human mobility. By leveraging the HOD data aggregation method, we can achieve more accurate population distribution estimates and capture the inherent dynamics of human movement

Dynamic stress analysis of rock joints under railway loading

The proper estimation of stresses generated by the passage of a train is fundamentally important to the serviceability and longevity of railways, and yet very limited knowledge is available where the track substructure is built on a jointed rock mass. The present study introduces an analytical solution for estimating the ground stresses arising from moving wheel loads, causing a change in the three-dimensional stress state in the track formation, in relation to the stress variation with depth and along the longitudinal track section – that is, the direction of train passage. Based on 21 case histories, an array of field measurements and numerical simulations covering a wide range of freight tonnage, train speeds and different formation conditions were considered to validate the proposed analytical solution. The proposed methodology (analytical solution) was then applied to a jointed rock subgrade to determine the normal and shear stresses acting along a specific discontinuity plane. The main analytical outcome demonstrates that the orthogonal vertical and shear stresses present different and phase-shifted history plots for homogeneous ground conditions with principal stresses rotation. However, conversely for a jointed subgrade, the normal and shear stresses along the discontinuity have the same history plot pattern and are in phase. As a practical guide, the results from this study would help to define which cyclic loads should be applied in laboratory tests to simulate realistic traffic patterns of trains travelling over a jointed rock subgrade.

Multi-Connectivity for Multicast Video Streaming in Cellular Networks

The escalating demand for high-quality video streaming poses a major challenge for communication networks today. Catering to these bandwidth-hungry video streaming services places a huge burden on the limited spectral resources of communication networks, limiting the resources available for other services as well. Large volumes of video traffic can lead to severe network congestion, particularly during live streaming events, which require sending the same content to a large number of users simultaneously. For such applications, multicast transmission can effectively combat network congestion while meeting the demands of all the users by serving groups of users requesting the same content over shared spectral resources. Streaming services can further benefit from multi-connectivity, which allows users to receive content from multiple base stations simultaneously. Integrating multi-connectivity within multicast streaming can improve the system resource utilization while also providing seamless connectivity to multicast users. Toward this end, this work studied the impact of using multi-connectivity (MC) alongside wireless multicast for meeting the resource requirements of video streaming. Our findings show that MC substantially enhances the performance of multicast streaming, particularly benefiting cell-edge users who often experience poor channel conditions. We especially considered the number of users that can be simultaneously served by multi-connected multicast systems. It was observed that about 60% of the users that are left unserved under single-connectivity multicast are successfully served using the same resources by employing multi-connectivity in multicast transmissions. We prove that the optimal resource allocation problem for MC multicast is NP-hard. As a solution, we present a greedy approximation algorithm with an approximation factor of (1−1/e). Furthermore, we establish that no other polynomial-time algorithm can offer a superior approximation. To generate realistic video traffic patterns in our simulations, we made use of traces from actual videos. Our results clearly demonstrate that multi-connectivity leads to significant enhancements in the performance of multicast streaming.

Feasibility of patient-specific quality assurance (PSQA) for real-time robotic stereotactic body radiotherapy (SBRT) based on tumor motion traces.

To design a patient specific quality assurance (PSQA) process for the CyberKnife Synchrony system and quantify its dosimetric accuracy using a motion platform driven by patient tumor traces with rotation. The CyberKnife Synchrony system was evaluated using a motion platform (MODUSQA) and a SRS MapCHECK phantom. The platform was programed to move in the superior-inferior (SI) direction based on tumor traces. The detector array housed by the StereoPhan was placed on the platform. Extra rotational angles in pitch (head down, 4.0°±0.15° or 1.2°±0.1°) were added to the moving phantom to examine robot capability of angle correction during delivery. A total of 15 Synchrony patients were performed SBRT PSQA on the moving phantom. All the results were benchmarked by the PSQA results based on static phantom. For smaller pitch angles, the mean gamma passing rates were 99.75%±0.87%, 98.63%±2.05%, and 93.11%±5.52%, for 3%/1mm, 2%/1mm, and 1%/1mm, respectively. Large discrepancy in the passing rates was observed for different pitch angles due to limited angle correction by the robot. For larger pitch angles, the corresponding mean passing rates were dropped to 93.00%±10.91%, 88.05%±14.93%, and 80.38%±17.40%. When comparing with the static phantom, no significant statistic difference was observed for smaller pitch angles (p=0.1 for 3%/1mm), whereas a larger statistic difference was observed for larger pitch angles (p<0.02 for all criteria). All the gamma passing rates were improved, if applying shift and rotation correction. The significance of this work is that it is the first study to benchmark PSQA for the CyberKnife Synchrony system using realistically moving phantoms with rotation. With reasonable delivery time, we found it may be feasible to perform PSQA for Synchrony patients with a realistic breathing pattern.

Impacts of Vertical Convective Mixing Schemes and Freshwater Forcing on the 2016–2017 Maud Rise Polynya Openings in a Regional Ocean Simulation

AbstractThe correct representation of the Maud Rise open‐ocean polynya in the Weddell Sea remains a challenge for ocean models. Here we reproduce the most recent polynya openings in 2016–2017 using a regional configuration, and assess their dependencies on vertical convective mixing schemes and freshwater forcing, both separately and in combination. We test three vertical convective mixing schemes: the enhanced vertical diffusion (EVD), the Eddy‐Diffusivity Mass‐Flux (EDMF) parameterization, and a modified version of EDMF accounting for thermobaric effects. Using simulations for the period 2007–2017, we find that the modified EDMF reproduces the observed climatological evolution of the mixed layer depth better than the original EDMF and the EVD, but a polynya fails to open due to excessive freshwater forcing. We thus use the modified EDMF to perform sensitivity experiments with reduced precipitation during 2012–2017. The imposed freshwater forcing strongly affects the number of years with polynyas. The simulation with the best representation of the 2016–2017 polynyas is analyzed to evaluate the triggering mechanisms. The 2016 polynya was induced by the action of thermobaric instabilities on a weak ambient stratification. This opening preconditioned the water column for 2017, which produced a stronger polynya. By examining the impacts of the different convective mixing schemes, we show that the modified EDMF generates more realistic patterns of deep convection. Our results highlight the importance of surface freshwater forcing and thermobaricity in governing deep convection around Maud Rise, and the need to represent thermobaric instabilities to realistically model Maud Rise polynyas.

Deterministic Global 3D Fractal Cloud Model for Synthetic Scene Generation

This paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal function. The fractal function is distorted by a displacement map, which is generated using horizontal wind data from a Global Forecast System (GFS) weather file. The vertical windspeed and relative humidity are used to mask the creation of clouds to match realistic large-scale weather patterns over the Earth. Small-scale detail is provided by the fractal functions which are tuned to match natural cloud shapes. This model is intended to run quickly, and it can run in about 700 ms per cloud type. This model generates clouds that appear to match large-scale satellite imagery, and it reproduces natural small-scale shapes. This should enable future versions of ASSET to generate scenarios where the same scene is consistently viewed from both GEO and LEO satellites from multiple perspectives.

Connectome spectrum electromagnetic tomography: A method to reconstruct electrical brain source networks at high-spatial resolution.

Connectome spectrum electromagnetic tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and two EEG datasets on visual-evoked potentials, we provide evidence supporting that (i) CSET captures realistic neurophysiological patterns with better accuracy than state-of-the-art methods, (ii) CSET can reconstruct brain responses more accurately and with more robustness to intrinsic noise in the EEG signal. These results demonstrate that CSET offers high spatio-temporal accuracy, enabling neuroscientists to extend their research beyond the current limitations of low sampling frequency in functional MRI and the poor spatial resolution of M/EEG.

Finite element modelling of refractories fracture process zone with gradient enhanced damage models

This study investigates the numerical simulation of fracture behaviour in quasi-brittle materials like magnesia spinel refractories using the Gradient-Enhanced Damage (GED) model. It focuses on the complex modelling of these materials non-linear responses and compares conventional and variant GED models through a wedge splitting test. The results demonstrate that all GED models show a good fit to experimental data. However, the conventional GED model falls short in accurately depicting the fracture process zone. In contrast, the localizing GED model more accurately represents the fracture process zone, limiting spurious damage distribution, but requires finer meshing, elevating computational demands. The stress-based variant reduces spurious damage but is less effective comparatively. The study also assesses the role of heterogeneous strength distribution in replicating realistic crack patterns as observed in experiments.

Mucin and salt combination simulate typical fern-like pattern of buffalo saliva smear at estrus.

Estrus detection in buffaloes primarily relies on behavioral and physiological signs. Especially during summer, these signs are less prominent to recognize. Thus, estrus detection is a pronounced challenge within the realm of buffalo husbandry, particularly in the summer. Therefore, a simple and accurate estrus detection method is required for buffalo farmers. The observation of fern-like salivary crystallization patterns is one such simple method to detect estrus in buffaloes, bactrian camels, beagle bitches, and cows. However, the exact mechanism for the formation of typical fern-like is not known. We hypothesized that it might be because of the estrus-specific mucins and salts. To test this hypothesis, we prepared the smears by combining different concentrations of mucin type -2 (MUC2) and -3 (MUC3) with sodium chloride (NaCl). Microscopic examination confirmed that fern-like patterns resulted from a combination of the MUC3 and NaCl produced more realistic fern patterns than that of MUC2 or BSA with salt. To predict possible mucin and salt concentration showing natural fern-like patterns at the estrus stage in buffalo saliva, we constructed a guide tree of artificially generated fern-like patterns using an image analysis online tool. This computation analysis revealed that most of the natural buffalo estrus saliva samples showing typical fern-like patterns clustered in the cluster 2 of the guide tree comprising of 13 clusters. In the cluster 2, MUC3 in combination with the salt concentrations of 100, 150, and 250 mM was commonly found in a close proximity to the natural typical fern-like patterns of saliva smear of buffaloes at estrus. Conclusively, the buffalo saliva at estrus is predicted to have a gel-forming heavily glycosylated protein such as mucin along with at least 100 mM of NaCl. RESEARCH HIGHLIGHTS: Glycoprotein and salts combination replicates fern-like pattern of buffalo saliva at estrus. MUC3 and NaCl salt combination produces more realistic fern-like patterns compared with MUC2 or BSA and salt combination. MUC3 with NaCl at 100, 150, and 250 mM consistently resembled natural estrus saliva fern-like patterns. During estrus, buffalo saliva is expected to contain heavily glycosylated mucin and at least of 100 mM NaCl.

Geometric effects of fragmentation are likely to mitigate diversity loss following habitat destruction in real‐world landscapes

AbstractAimHabitat conversion is the number one threat to biodiversity. The loss of biodiversity due to habitat loss might be exacerbated if species are harmed by fragmentation per se—the breaking apart of natural habitat that remains (hereafter fragmentation). However, the evidence that species are harmed by habitat fragmentation is mixed. Studies at the patch scale tend to show that fragmentation reduces diversity due to negative demographic effects on species' dispersal, survival and fecundity. In contrast, studies at the landscape scale tend to show that fragmentation increases diversity. This discrepancy may be partly due to geometric effects, defined as greater species turnover between patches in more fragmented landscapes. Although these effects have been demonstrated theoretically and are expected to be stronger across larger spatial extents, it is unclear whether they are likely to occur in real‐world settings with both realistic landscape patterns and communities. Here, we investigated the possibility of geometric effects using simulations combined with real‐world landscape and community data.LocationNew Jersey, northeastern USA.Time periodCurrent.Taxa studiedBees.MethodsWe focused on landscape sizes within the typical range for protected areas (36–576 ha), simulated forest loss using real landscape patterns, and simulated forest‐bee communities based on field data we collected.ResultsWe found weak but positive effects of fragmentation: immediately following forest destruction, the most fragmented forests harboured up to 7.3% more species than the least fragmented forests of the same area, in agreement with observational studies of biodiversity along fragmentation gradients. In contrast to expectations, however, the overall effects of fragmentation did not change with spatial extent.ConclusionsOur results suggest that fragmentation can mitigate biodiversity loss immediately following habitat destruction, but that the benefits do not vary strongly with spatial extent in real‐world landscapes and at extents relevant to land management.

Acetylcholine esterase of Drosophila melanogaster: a laboratory model to explore insecticide susceptibility gene drives.

One of the proposed applications of gene drives has been to revert pesticide resistant mutations back to the ancestral susceptible state. Insecticides that have become ineffective because of the rise of resistance could have reinvigorated utility and be used to suppress pest populations again, perhaps at lower application doses. We have created a laboratory model for susceptibility gene drives that replaces field-selected resistant variants of the acetylcholine esterase (Ace) locus of Drosophila melanogaster with ancestral susceptible variants. We constructed a CRISPR/Cas9 homing drive and found that homing occurred in many genetic backgrounds with varying efficiencies. While the drive itself could not be homozygous, it converted resistant alleles into susceptible ones and produced recessive lethal alleles that could suppress populations. Our studies provided evidence for two distinct classes of gene drive resistance (GDR): rather than being mediated by the conventional non-homologous end-joining (NHEJ) pathway, one seemed to involve short homologous repair and the other was defined by genetic background. Additionally, we used simulations to explore a distinct application of susceptibility drives; the use of chemicals to prevent the spread of synthetic gene drives into protected areas. Insecticide susceptibility gene drives could be useful tools to control pest insects however problems with particularities of target loci and GDR will need to be overcome for them to be effective. Furthermore, realistic patterns of pest dispersal and high insecticide exposure rates would be required if susceptibility were to be useful as a 'safety-switch' to prevent the unwanted spread of gene drives. © 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks

This paper presents a study on connection-blocking prediction in Elastic Optical Networks (EONs) using Convolutional Neural Networks (CNNs). In EONs, connections are established and torn down dynamically to fulfill the instantaneous requirements of the users. The dynamic allocation of the connections may cause spectrum fragmentation and lead to network performance degradation as connection blocking increases. Predicting potential blocking situations can be helpful during EON operations. For example, this prediction could be used in real networks to trigger proper spectrum defragmentation mechanisms at suitable moments, thereby enhancing network performance. Extensive simulations over the well-known NSFNET (National Science Foundation Network) backbone network topology were run by generating realistic traffic patterns. The obtained results are later used to train the developed machine learning models, which allow the prediction of connection-blocking events. Resource use was continuously monitored and recorded during the process. Two different Convolutional Neural Network models, a 1D CNN (One-Dimensional Convolutional Neural Network) and 2D CNN (Two-Dimensional Convolutional Neural Network), are proposed as the predicting methods, and their behavior is compared to other conventional models based on an SVM (Support Vector Machine) and KNN (K Nearest Neighbors). The results obtained show that the proposed 2D CNN predicts blocking with the best accuracy (92.17%), followed by the SVM, the proposed 1D CNN, and KNN. Results suggest that 2D CNN can be helpful in blocking prediction and might contribute to increasing the efficiency of future EON networks.

A finite element simulation approach for glued-laminated timber beams using continuum-damage model and sequentially linear analysis

Glued-laminated timber (GLT) is an efficient and renewable material with potential to enhance sustainability in construction, but its potential has been partly hindered by relatively large uncertainty related to its strength. Previous research indicate that by linking information from the grading process to the GLT fabrication more reliable GLT beams could be made. To this end, this paper represents a finite element based modelling approach combining a continuum damage model with sequentially linear analysis that is suitable for simulation of the bending stiffness and strength of GLT beams. The model has been implemented in Matlab and can be easily integrated into different parametric simulation workflows. Based on validation against 36 experimentally tested GLT beams, the model can predict bending stiffness with high accuracy and provides reasonably accurate predictions for bending strength. The model is also capable of producing qualitatively realistic fracture patterns of GLT beams. The mesh-size study suggests that the best agreement for bending strength with test data is obtained with a sparse mesh, which is discussed in detail. The model is considered suitable for follow-up studies involving the optimization of GLT beam layups.

Numerical Modeling and Analysis of Transient and Three-Dimensional Heat Transfer in 3D Printing via Fused-Deposition Modeling (FDM)

Fused-Deposition Modeling (FDM) is a commonly used 3D printing method for rapid prototyping and the fabrication of plastic components. The history of temperature variation during the FDM process plays a crucial role in the degree of bonding between layers. This study presents research on the thermal analysis of the 3D printing process using a developed simulation code. The code employs numerical discretization methods with an implicit scheme and an effective heat transfer coefficient for cooling. The computational model is validated by comparing the results with analytical solutions, demonstrating an agreement of more than 99%. The code is then utilized to perform thermal analyses for the 3D printing process. Interlayer and intralayer reheating effects, sensitivity to printing parameters, and realistic printing patterns are investigated. It is shown that concentric and zigzag paths yield similar peaks at different time intervals. Nodal temperatures can fall below the glass transition temperature (Tg) during the printing process, especially at the outer nodes of the domain and under conditions where the cooling period is longer and the printed volume per unit time is smaller. The article suggests future work to calculate welding time at different conditions and locations for the estimation of the degree of bonding.

Size-dependence of food intake and mortality interact with temperature and seasonality to drive diversity in fish life histories.

Understanding how growth and reproduction will adapt to changing environmental conditions is a fundamental question in evolutionary ecology, but predicting the responses of specific taxa is challenging. Analyses of the physiological effects of climate change upon life history evolution rarely consider alternative hypothesized mechanisms, such as size-dependent foraging and the risk of predation, simultaneously shaping optimal growth patterns. To test for interactions between these mechanisms, we embedded a state-dependent energetic model in an ecosystem size-spectrum to ask whether prey availability (foraging) and risk of predation experienced by individual fish can explain observed diversity in life histories of fishes. We found that asymptotic growth emerged from size-based foraging and reproductive and mortality patterns in the context of ecosystem food web interactions. While more productive ecosystems led to larger body sizes, the effects of temperature on metabolic costs had only small effects on size. To validate our model, we ran it for abiotic scenarios corresponding to the ecological lifestyles of three tuna species, considering environments that included seasonal variation in temperature. We successfully predicted realistic patterns of growth, reproduction, and mortality of all three tuna species. We found that individuals grew larger when environmental conditions varied seasonally, and spawning was restricted to part of the year (corresponding to their migration from temperate to tropical waters). Growing larger was advantageous because foraging and spawning opportunities were seasonally constrained. This mechanism could explain the evolution of gigantism in temperate tunas. Our approach addresses variation in food availability and individual risk as well as metabolic processes and offers a promising approach to understand fish life-history responses to changing ocean conditions.

General Local Reactive Boundary Condition for Dissolution and Precipitation Using the Lattice Boltzmann Method

AbstractA general and local reactive boundary condition (RBC) for studying first‐order equilibrium reactions using the lattice Boltzmann method is presented. Its main characteristics are accurate reproduction of wall diffusion, invariance to the wall and grid orientation, and absence of nonphysical artifacts. The scheme is successfully tested for different benchmark cases considering diffusion, advection, and reactions of fluids at solid‐liquid interfaces. Unlike other comparable RBCs from the literature, the novel scheme is valid for a large range of Péclet and Damköhler numbers, and shows realistic pattern formation during precipitation. In addition, quantitative results are in good accordance with analytical solutions and values from literature. Combining the new RBC with the rest fraction method, Péclet‐Reynolds ratios of up to 1,000 can be achieved. Overall, the novel RBC accurately models first‐order reactions, is applicable for complex geometries, and allows efficiently simulating dissolution and precipitation phenomena in fluids at the pore scale.

CFPD-PK simulation of inhaled Delta-9-tetrahydrocannabinol aerosol dynamics: Transport, deposition, and translocation in a mouth-to-G10 subject-specific human airway

Medical cannabis is increasingly used as an alternative therapy for various conditions, including chronic pain, multiple sclerosis, epilepsy, and cancer-related symptoms. However, it is crucial to ensure that patients receive the intended dose of tetrahydrocannabinol (THC) from inhaled cannabis for optimal therapeutic effect without overdose risk. This requires a comprehensive understanding of the factors that influence the pharmacokinetics (PKs) of THC in the respiratory system. However, accurate estimation of lung dosimetry and blood concentration of inhaled THC remains challenging partially because the influence of diversified patient-specific puff patterns on inhaled THC transport, deposition, and translocation is still not well quantified. To address the knowledge gap mentioned above, this study employed a hybrid computational fluid-particle dynamics (CFPD) and PK model to evaluate factors that influence delivered doses of THC to the human respiratory system and the resultant THC-plasma concentration-time profile. Specifically, this study compared multiple puff waveforms for inhalation-holding-exhalation (IHE) with total puff volumes from 55 to 82 ml for 2 or 3 s, hold durations from 0 to 5 s, and three puff waveforms (i.e., square, sinusoidal, and realistic). THC deposition in the airways was recorded during all phases for each case using either 452,849 particles per second for the 1.128-μm monodisperse cases or 399,866 particles per second for the polydisperse cases, with the mass median aerodynamic diameter (MMAD) of 1.128 μm. Pulmonary air-THC particle flow transport dynamics, THC particle deposition data, and THC vapor absorption were predicted using CFPD for four airway regions, then scaled by region-specific bioavailability factors. The deposited THC mass in airway regions represents the initial mass entering a 3-compartment PK model, to predict the THC-plasma concentration-time profiles. The CFPD-PK results revealed significant variability in THC transport, deposition, and plasma concentration based on IHE factors. Specifically, larger puff volumes and longer holding times enhanced THC deposition in deeper airways and increased THC-plasma concentrations. Realistic transient puff waveforms predicted higher particle deposition and THC-plasma concentrations than simplified square waveforms. Polydisperse particle distributions show more realistic deposition patterns than monodisperse particle simulations. This study provides insights into the complexity of THC inhalation therapy, emphasizing the importance of considering individualized puff patterns and realistic particle size distributions in accurately predicting therapeutic outcomes, which is highly related to THC deposition in the lung and THC plasma concentration. These findings and the CFPD-PK modeling framework offer guidance for clinicians in prescribing personalized THC dosages, support regulatory science in evaluating inhalation devices, and contribute to ongoing research aimed at optimizing THC delivery for maximum therapeutic effectiveness while minimizing potential overdose risks.

Neural Manifold Constraint for Spike Prediction Models Under Behavioral Reinforcement.

Spike prediction models effectively predict downstream spike trains from upstream neural activity for neural prostheses. Such prostheses could potentially restore damaged neural communication pathways using predicted patterns to guide electrical stimulations on downstream. Since the ground truth of downstream neural activity is unavailable for subjects with the damage, reinforcement learning (RL) with behavior-level rewards becomes necessary for model training. However, existing models do not involve any constraint on the generated firing patterns and neglect the correlations among neural activities. Thus, the model outputs can greatly deviate from the natural range of neural activities, causing concerns for clinical usage. This study proposes the neural manifold constraint to solve this problem, shaping RL-generated spike trains in the feature space. The constraint terms describe the first and second order statistics of the neural manifold estimated from neural recordings during subjects' freely moving period. Then, the models can be optimized within the neural manifold by behavioral reinforcement. We test the method to predict primary motor cortex (M1) spikes from medial prefrontal (mPFC) spikes when rats perform the two-lever discrimination task. Results show that the neural activity generated by constrained models resembles the real M1 recordings. Compared with models without constraints, our approach achieves similar behavioral success rates, but reduces the mean squared error of neural firing by 61%. The constraints also increase the model's robustness across data segments and induce realistic neural correlations. Our method provides a promising tool to restore transregional communication with high behavioral performance and more realistic microscopic patterns.

The type of inhibition provided by thalamic interneurons alters the input selectivity of thalamocortical neurons

A fundamental problem in neuroscience is how neurons select for their many inputs. A common assumption is that a neuron's selectivity is largely explained by differences in excitatory synaptic input weightings. Here we describe another solution to this important problem. We show that within the first order visual thalamus, the type of inhibition provided by thalamic interneurons has the potential to alter the input selectivity of thalamocortical neurons. To do this, we developed conductance injection protocols to compare how different types of synchronous and asynchronous GABA release influence thalamocortical excitability in response to realistic patterns of retinal ganglion cell input. We show that the asynchronous GABA release associated with tonic inhibition is particularly efficient at maintaining information content, ensuring that thalamocortical neurons can distinguish between their inputs. We propose a model where alterations in GABA release properties results in rapid changes in input selectivity without requiring structural changes in the network.

Impact of Multi-Grade Localized Calcifications on Aortic Valve Dynamics under Helical Inflow: A Comparative Hemodynamic Study

This study investigates the hemodynamic impacts of localized aortic valve calcification, utilizing immersed boundary-finite element (IBFE) method simulations with realistic inflow patterns of uniform and helical blood flow from the left ventricular outflow tract (LVOT). We modeled the aortic valve leaflets with varying grades of calcification, assessing their influence on valve performance, including transvalvular hemodynamics, wall shear stress (WSS) indices, and vortical structures. The findings highlighted that calcification significantly restricts leaflet motion, diminishes the orifice area, disrupts flow efficiency, and consequently increases the left ventricular workload. Advanced calcification resulted in elevated WSS, especially at the leaflet tips, which indicates a heightened risk of endothelial damage and further calcification. Asymmetrical calcifications redirect flow towards the ascending aorta wall, potentially inducing structural damage and increased stress on the remaining healthy leaflets. Calcification was also found to alter the naturally occurring helical blood flow patterns, affecting the system’s fluid transport efficiency and possibly contributing to cardiovascular disease progression. The study revealed a significant alteration in vortex formation, with calcification causing distorted and complex vortex structures, which may influence the dynamics of blood flow and valve function. These insights into the hemodynamic changes induced by calcification contribute to a better understanding of the progression of aortic valve diseases and could inform more effective diagnostic and treatment strategies.