Local Geometry Research Articles

Fluid-Dynamic Modeling of Flow in Embryonic Tissue Indicates That Lymphatic Valve Location Is Not Consistently Determined by the Local Fluid Shear or Its Gradient.

Intravascular lymphatic valves often occur in proximity to vessel junctions. It is commonly held that disturbed flow at junctions is responsible for accumulation of valve-forming cells (VFCs) at these locations as the initial step in valve creation, and the one which explains the association with these sites. However, evidence in favor is largely limited to cell culture experiments. We acquired images of embryonic lymphatic vascular networks from day E16.5, when VFC accumulation has started but the developing valve has not yet altered the local vessel geometry, stained for Prox1, which co-localizes with Foxc2. Using finite-element computational fluid mechanics, we simulated the flow through the networks, under conditions appropriate to this early development stage. Then we correlated the Prox1 distributions with the distributions of simulated fluid shear and shear stress gradient. Across a total of 16 image sets, no consistent correlation was found between Prox1 distribution and the local magnitude of fluid shear, or its positive or negative gradient. This, the first direct semi-empirical test of the localization hypothesis to interrogate the tissue from invivo at the critical moment of development, does not support the idea that a feature of the local flow determines valve localization.

Energy extraction via magnetic reconnection in magnetized black holes

The Comisso-Asenjo mechanism is a novel mechanism proposed recently to extract energy from black holes through magnetic reconnection of the surrounding charged plasma, in which the magnetic field plays a crucial role. In this work, we revisit this process by taking into account the backreaction of the magnetic field on the black hole's geometry. We employ the Kerr-Melvin metric to describe the local near-horizon geometry of the magnetized black hole. By analyzing the circular orbits in the equatorial plane, energy extraction conditions, power and efficiency of the energy extraction, we found that while a stronger magnetic field can enhance plasma magnetization and aid energy extraction, its backreaction on the spacetime may hinder the process, with a larger magnetic field posing a greater obstacle. Balancing these effects, an optimal moderate magnetic field strength is found to be most conducive to energy extraction. Moreover, there is a maximum limit to the magnetic field strength associated with the black hole's spin, beyond which circular orbits in the equatorial plane are prohibited, thereby impeding energy extraction in the current scenario.

A physics-informed machine learning model for the prediction of drop breakup in two-phase flows

Predictive simulations of two-phase flows are highly sought after because of their widespread applications in propulsion, energy, agriculture, and medicine. One crucial goal for many of these simulations is the accurate and efficient prediction of the size distribution and number density of atomized drops. The multi-scale nature of these flows makes it practically impossible to capture all scales within a single simulation. In particular, the breakup processes producing the smallest drops through secondary breakup often necessitate resolutions far below the Kolmogorov scale. Consequently, models must be employed for secondary breakup. Existing physics-based and stochastic breakup models are not universal and fail to account for the local and instantaneous flow field and drop geometry. We present a physics-informed machine learning model for predicting the statistics of daughter drops generated during the breakup of under-resolved drops. By training on high-fidelity simulations, the model can predict breakup outcomes from severely under-resolved input fields. This is made possible by a careful choice of quantities of interest and by taking inspiration from the discrete nature of breakup events to encode the temporal evolution via a mixture of sigmoid functions. We showcase proof-of-concept results from the canonical settings of 3D Taylor–Green vortex flows and homogeneous isotropic turbulence. Compared to results generated by low-resolution simulations (i.e., without a model) and baseline state-of-the-art models, our approach achieves superior accuracy in predicting drop size distribution and critical quantities of interest, such as surface area. This work paves the path towards the development of embedded machine learning models for the prediction of primary and secondary breakup.

Task structure tailors the geometry of neural representations in human lateral prefrontal cortex.

How do human brains represent tasks of varying structure? The lateral prefrontal cortex (lPFC) flexibly represents task information. However, principles that shape lPFC representational geometry remain unsettled. We use fMRI and pattern analyses to reveal the structure of lPFC representational geometries as humans perform two distinct categorization tasks- one with flat, conjunctive categories and another with hierarchical, context-dependent categories. We show that lPFC encodes task-relevant information with task-tailored geometries of intermediate dimensionality. These geometries preferentially enhance the separability of task-relevant variables while encoding a subset in abstract form. Specifically, in the flat task, a global axis encodes response-relevant categories abstractly, while category-specific local geometries are high-dimensional. In the hierarchy task, a global axis abstractly encodes the higher-level context, while low-dimensional, context-specific local geometries compress irrelevant information and abstractly encode the relevant information. Comparing these task geometries exposes generalizable principles by which lPFC tailors representations to different tasks.

Geometrical interpretation of isospin subalgebras in SU(3)

Ever since new tetrads in four-dimensional Lorentzian curved spacetimes were introduced, many outstanding properties and results have been proven regarding Riemannian geometry, gauge theory or group theory. These tetrads might carry spacetime-kinematic information about particle multiplets. Among other properties, these new tetrads, locally and covariantly diagonalize stress–energy tensors. In the case where particles have an electromagnetic field, the local planes of gauge symmetry served as a tool in order to propose a new gravitational-kinematic interpretation of particle multiplets. The core reason stems from the mathematical fact that all local gauge symmetries associated to the Standard Model have been proven to be isomorphic to local groups of tetrad transformations in four-dimensional Lorentzian spacetimes. Therefore, the different stress–energy tensors have been proven to determine the local gauge geometry as a natural part of Riemannian geometry. The stress–energy tensors determine locally the orthogonal planes such that the rotation on either of them of the local tetrad vectors that span these planes is isomorphic to local tetrad gauge transformations. All these properties put together allow for a reinterpretation presented previously of particle states as particle gravitational-kinematic-spacetime tetrad states in the asymptotically flat limit. We proceed in this paper to study the case where there are [Formula: see text], [Formula: see text] and [Formula: see text] isospin subalgebras or submultiplets within the context of this new Riemannian interpretation of gauge symmetries.

Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge.

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein-nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

Systolic almost-rigidity modulo 2

No power law systolic freedom is possible for the product of mod 2 systoles of dimension 1 and codimension 1 . This means that any closed n -dimensional Riemannian manifold M of bounded local geometry obeys the following systolic inequality: the product of its mod 2 systoles of dimensions 1 and n-1 is bounded from above by c(n,\varepsilon) \,\mathrm{Vol}(M)^{1+\varepsilon} , if finite (if H_{1}(M; \mathbb{Z}/2) is non-trivial).

Beyond local patches: Preserving global–local interactions by enhancing self-attention via 3D point cloud tokenization

Transformer-based architectures have recently shown impressive performance on various point cloud understanding tasks such as 3D object shape classification and semantic segmentation. Particularly, this can be attributed to their self-attention mechanism, which has the ability to capture long-range dependencies. However, current methods have constrained it to operate in local patches due to its quadratic memory constraints. This hinders their generalization ability and scaling capacity due to the loss of non-locality in early layers. To tackle this issue, we propose a window-based transformer architecture that captures long-range dependencies while aggregating information in the local patches. We do this by interacting each window with a set of global point cloud tokens — a representative subset of the entire scene — and augmenting the local geometry through a 3D Histogram of Oriented Gradients (HOG) descriptor. Through a series of experiments on segmentation and classification tasks, we show that our model exceeds the state-of-the-art on S3DIS semantic segmentation (+1.67% mIoU), ShapeNetPart part segmentation (+1.03% instance mIoU) and performs competitively on ScanObjectNN 3D object classification.11The code and trained models shall be made publicly available.

Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation

Deep learning (DL)-based methods have achieved state-of-the-art performance for many medical image segmentation tasks. Nevertheless, recent studies show that deep neural net- works (DNNs) can be miscalibrated and overconfident, leading to ”silent failures” that are risky for clinical applications. Bayesian DL provides an intuitive approach to DL failure de- tection, based on posterior probability estimation. However, the posterior is intractable for large medical image segmentation DNNs. To tackle this challenge, we propose a Bayesian learning framework using Hamiltonian Monte Carlo (HMC), tempered by cold posterior (CP) to accommodate medical data augmentation, named HMC-CP. For HMC compu- tation, we further propose a cyclical annealing strategy, capturing both local and global geometries of the posterior distribution, enabling highly efficient Bayesian DNN training with the same computational budget as training a single DNN. The resulting Bayesian DNN outputs an ensemble segmentation along with the segmentation uncertainty. We evaluate the proposed HMC-CP extensively on cardiac magnetic resonance image (MRI) segmentation, using in-domain steady-state free precession (SSFP) cine images as well as out-of-domain datasets of quantitative T1 and T2 mapping. Our results show that the proposed method improves both segmentation accuracy and uncertainty estimation for in- and out-of-domain data, compared with well-established baseline methods such as Monte Carlo Dropout and Deep Ensembles. Additionally, we establish a conceptual link between HMC and the commonly known stochastic gradient descent (SGD) and provide general insight into the uncertainty of DL. This uncertainty is implicitly encoded in the training dynamics but often overlooked. With reliable uncertainty estimation, our method provides a promising direction toward trustworthy DL in clinical applications. We release our code in <a href='https://gitlab.tudelft.nl/yidongzhao/hmc_uncertainty'>https://gitlab.tudelft.nl/yidongzhao/hmc_uncertainty</a>

Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters.

Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This study introduces a comprehensive asymptotic parameter database encompassing atomic multipole moments, polarizabilities, and dispersion coefficients. Leveraging active learning, our database comprehensively represents protein fragments with up to 8 heavy atoms, capturing their conformational diversity with merely 78,000 data points. Additionally, the E(3) neural network (E3NN) is employed to predict the asymptotic parameters directly from the local geometry. The E3NN models demonstrate exceptional accuracy and transferability across all asymptotic parameters, achieving an R2 of 0.999 for both protein fragments and 20 amino acid dipeptide test sets. The long-range electrostatic and dispersion energies can be obtained using the E3NN-predicted parameters, with an error of 0.07 and 0.02 kcal/mol, respectively, when compared to symmetry-adapted perturbation theory (SAPT). Therefore, our force fields demonstrate the capability to accurately describe long-range interactions in proteins, paving the way for next-generation protein force fields.

Magnetic behavior of 3D interconnect nanoporous FeCo synthesized by liquid metal dealloying

Liquid Metal Dealloying (LMD) is tested to enhance the near-surface magnetic properties of ferromagnetic Iron-Cobalt alloys. With a lower surface electrical conductivity, treated specimens are expected to accommodate the frequency effect better and exhibit a lower level of magnetic loss. This work focuses on the magnetic viability of LMD-treated samples. The magnetic hysteresis cycles of virgin, fully-dealloyed, and partially-dealloyed specimens are measured from quasi-static to dynamic regimes. For each category of specimens, experimental results are compared with numerical predictions given by dedicated hysteresis models. The simulation parameters of the fully-dealloyed and the virgin specimens are leveraged to predict the partially-dealloyed specimen's behavior and estimate the thickness of the treated area accurately and non-destructively. Together with SEM observations and finite element simulations, these results show that the magnetic behavior of the metallic part in the dealloyed layer is barely affected by the specimen porosity, but the average variations observed experimentally are primarily due to the distortion of the magnetic lines and changes in the local geometry.

Correlating Local Geometry to 11B NMR Chemical Shifts of Tetrahedrally Coordinated Boron in Molecular Sieves

Correlating Local Geometry to ¹¹B NMR Chemical Shifts of Tetrahedrally Coordinated Boron in Molecular Sieves

A new discrete element method for small adhesive non-spherical particles

Small adhesive particles with non-spherical shapes are common in natural and industrial processes. However, it is challenging to accurately simulate the dynamic behaviors of non-spherical particulate systems, not only due to the complex local geometry at the contact region but also to the non-linear coupling between the van der Waals adhesion and the elastic deformation. In this paper, we develop a discrete element method (DEM) framework for small non-spherical particles. Particularly, we present a simple algorithm for solving the implicit equations of the dimensionless generalized JKR model, to accurately resolve the local surface geometry at the contact region and the strong coupling between the elastic deformation and the surface adhesion. New explicit expressions are established for the pull-off force, overlap and minor-to-major ratio of the contact ellipse, to naturally capture the pull-off point upon collision. Then the actual contact parameters including the contact radius, curvatures and overlap are used to modify the interparticle interactions, including the normal elastic-adhesive-dissipative force and sliding-rolling-twisting resistances. Finally, the framework is quantitatively validated by four random packing problems, including adhesive spheres, ellipsoids, spherocylinders and dimers, and the representative non-adhesive ellipsoids. Both the microscopic and macroscopic properties of packings agree well with a large number of classic theories, experiments and simulations, showing the satisfactory accuracy and effectiveness. The newly developed adhesive DEM framework has the full capability to dynamically simulate the collision-related behaviors of complex shaped particles in the presence of van der Waals adhesion and frictional forces.

Advancing the Limits of InSAR to Detect Crustal Displacement from Low-Magnitude Earthquakes through Deep Learning

Detecting surface deformation associated with low-magnitude (Mw≤5) seismicity using interferometric synthetic aperture radar (InSAR) is challenging due to the subtlety of the signal and the often challenging imaging environments. However, low-magnitude earthquakes are potential precursors to larger seismic events, and thus characterizing the crustal displacement associated with them is crucial for regional seismic hazard assessment. We combine InSAR time-series techniques with a Deep Learning (DL) autoencoder denoiser to detect the magnitude and extent of crustal deformation from the Mw=3.4 Gallina, New Mexico earthquake that occurred on 30 July 2020. Although InSAR alone cannot detect event-related deformation from such a low-magnitude seismic event, application of the DL method reveals maximum displacements as small as (±2.5 mm) in the vicinity of both the fault and earthquake epicenter without prior knowledge of the fault system. This finding improves small-scale displacement discernment with InSAR by an order of magnitude relative to previous studies. We additionally estimate best-fitting fault parameters associated with the observed deformation. The application of the DL technique unlocks the potential for low-magnitude earthquake studies, providing new insights into local fault geometries and potential risks from higher-magnitude earthquakes. This technique also permits low-magnitude event monitoring in areas where seismic networks are sparse, allowing for the possibility of global fault deformation monitoring.

Simulation of particle-laden flows and erosion in an axial fan stage considering the relative position of the blades

Abstract Axial fans are vital accessories in aircraft ventilation systems, but, they may experience erosion from particulate flows, causing a decline in effectiveness over time. This study investigated the trajectories of two types of sand particles and erosion in an axial fan stage, considering the relative position of the blades facing the inlet guide vanes. The movement of particles was simulated using an in-house code that implements a Lagrangian approach along with a stochastic particle-eddy interaction model. The flow field was solved separately and the flow data was transferred to the particle trajectory code. The finite element method allowed for the tracking of particles through the computational cells and accurate determination of their impact positions. A semi-empirical erosion correlation was used to evaluate the local erosion rates, mass removal, and geometry deterioration. As a result, the rotor exhibits a high frequency of impacts and significant erosion on the leading edge of the blade, extending to the upper corner of the pressure side and blade tip, as well as the front of the suction side. In the inlet guide vane, the erosion is spread out along the entire pressure side but at lower erosion rates compared to the rotor blade. The erosion patterns obtained at different pitch-wise positions were cumulated to get better representation of erosion patterns. After being exposed to MIL-E5007E sand (0–1000 $\unicode{x03BC}$ m) at the highest concentration for 10 hours, the blade experienced a reduction of a 0.29% in mass, a 0.45% decrease in tip chord, and a 0.23% increase in tip clearance. On the other hand, AC-coarse sand (0–200 μm) resulted in a 0.23% decrease in blade mass, a 0.4% reduction in tip chord, and a 0.16% increase in tip clearance. The data that is available can be used to monitor the lifespan of axial fans of similar design and select appropriate coatings to protect against erosion.

Influence of peripheral axon geometry and local anatomy on magnetostimulation chronaxie

Objective. Rapid switching of magnetic resonance imaging (MRI) gradient fields induces electric fields that can cause peripheral nerve stimulation (PNS) and so accurate characterization of PNS is required to maintain patient safety and comfort while maximizing MRI performance. The minimum magnetic gradient amplitude that causes stimulation, the PNS threshold, depends on intrinsic axon properties and the spatial and temporal properties of the induced electric field. The PNS strength–duration curve is widely used to characterize simulation thresholds for periodic waveforms and is parameterized by the chronaxie and rheobase. Safety limits to avoid unwanted PNS in MRI rely on a single chronaxie value to characterize the response of all nerves. However, experimental magnetostimulation peripheral nerve chronaxie values vary by an order of magnitude. Given the diverse range of chronaxies observed and the importance of this number in MRI safety models, we seek a deeper understanding of the mechanisms contributing to chronaxie variability. Approach. We use a coupled electromagnetic-neurodynamic PNS model to assess geometric sources of chronaxie variability. We study the impact of the position of the stimulating magnetic field coil relative to the body, along with the effect of local anatomical features and nerve trajectories on the driving function and the resulting chronaxie. Main results. We find realistic variation of local axon and tissue geometry can modulate a given axon’s chronaxie by up to two-fold. Our results identify the temporal rate of charge redistribution as the underlying determinant of the chronaxie. Significance. This charge distribution is a function of both intrinsic axon properties and the spatial stimulus along the nerve; thus, examination of the local tissue topology, which shapes the electric fields, as well as the nerve trajectory, are critical for better understanding chronaxie variations and defining more biologically informed MRI safety guidelines.

What Controls Early Restraining Bend Growth? Structural, Morphometric, and Numerical Modeling Analyses From the Eastern California Shear Zone

AbstractRestraining bends influence topography, strike‐slip evolution, and earthquake rupture dynamics, however the specific factors governing their geometry and development in the crust are not well established. These relationships are challenging to investigate in field examples due to cannibalization and erosion of earlier structures with cumulative strain. To address this knowledge gap, we investigated the structure, morphology, and kinematics of 22 basement‐cored restraining bends on low net‐slip faults (<10 km) within the southern Eastern California shear zone (SECSZ) via mapping, topographic analyses, and 3D numerical modeling. The bends are self‐similar in form with most exhibiting focused relief between high‐angle bounding faults with an arrowhead shape in map view and a “whaleback” longitudinal profile. Slight changes in that form occur with increasing size indicating predictable growth that appears to be primarily controlled by local fault geometries (i.e., bifurcation angle), rather than the influence of fault obliquity relative to far‐field plate motion, due to inefficient slip‐transfer across interconnected irregularly trending closely spaced faults. Modeling results indicate that the self‐similar fault‐bound geometry of SECSZ restraining bends may arise from elevated shear strain at the outer corners of single transpressional fault bends with increasing cumulative slip. This, in turn, promotes growth of a new fault leading to efficient accommodation of local convergent strain via uplift between bounding faults. Finally, our results indicate that the kilometer‐scale restraining bends contribute minimally to regional contraction as they only penetrate the upper third of the seismogenic crust and are therefore also unlikely to impede large earthquake surface ruptures.

High-resolution in situ structures of mammalian respiratory supercomplexes

Mitochondria play a pivotal part in ATP energy production through oxidative phosphorylation, which occurs within the inner membrane through a series of respiratory complexes1–4. Despite extensive in vitro structural studies, determining the atomic details of their molecular mechanisms in physiological states remains a major challenge, primarily because of loss of the native environment during purification. Here we directly image porcine mitochondria using an in situ cryo-electron microscopy approach. This enables us to determine the structures of various high-order assemblies of respiratory supercomplexes in their native states. We identify four main supercomplex organizations: I1III2IV1, I1III2IV2, I2III2IV2 and I2III4IV2, which potentially expand into higher-order arrays on the inner membranes. These diverse supercomplexes are largely formed by ‘protein–lipids–protein’ interactions, which in turn have a substantial impact on the local geometry of the surrounding membranes. Our in situ structures also capture numerous reactive intermediates within these respiratory supercomplexes, shedding light on the dynamic processes of the ubiquinone/ubiquinol exchange mechanism in complex I and the Q-cycle in complex III. Structural comparison of supercomplexes from mitochondria treated under different conditions indicates a possible correlation between conformational states of complexes I and III, probably in response to environmental changes. By preserving the native membrane environment, our approach enables structural studies of mitochondrial respiratory supercomplexes in reaction at high resolution across multiple scales, from atomic-level details to the broader subcellular context.

Effects of different types of fibrotic remodelling in areas of low voltage on atrial fibrillation dynamics: an in-silico study

Abstract Background Personalized computational models include the effects of personalised anatomy and atrial fibrillation (AF) remodelling in a framework that can be used to investigate patient-specific AF mechanisms and test treatment response, but modelling fibrosis remains an unresolved matter. The interpretation of regions exhibiting low voltage in electroanatomic mapping data at different pacing rates also remain unclear. However, these occurrences are correlated with fibrotic remodelling. We aimed to use patient-specific simulations to investigate the effects of fibrosis type on AF properties by modelling conduction velocity slowing, changes in ionic channels, and replacement fibrosis, incorporated in regions of low voltage identified through omnipolar mapping at different pacing rates. Methods Local activation times (LATs) voltage and geometry data were obtained from patients undergoing ablation for persistent AF. LATs were collected in sinus rhythm with coronary sinus pacing at pacing interval (PIs) of 250ms and 600ms. For a total of 10 cases, personalised anatomical models were constructed for electrophysiology simulations, and analysed using an automated pipeline in python. For conductivity models, areas with peak omnipolar voltage (OV) below 0.5mV were assigned low conduction velocity. To incorporate ionic changes, the ionic properties were altered in fibrotic regions (peak OV < 0.5mV). In the simulations where replacement fibrosis was included through a probabilistic percolation method, percolation was modelled by using random number generator to probabilistically remove elements of low voltage. Pulmonary vein isolation was simulated in each model after 5s of AF, and wavefront propagation patterns were analysed to quantify the number of rotational areas or areas of wavefront break-up in 7 anatomical segments. Results The models with conductivity fibrotic remodelling demonstrated rotational drivers in more than two LA anatomical segments for both CS250ms pacing rate and CS600ms pacing rate across all 10 cases. The anterior wall exhibited the largest degree of rotational activity in 7 out of 10 cases for CS250ms pacing rate and in 9 out of 10 cases for CS600ms pacing rate (Figure A). The combination of percolation and conductivity remodelling also exhibited rotational activity on the anterior wall in 9 out of 10 cases at CS250ms pacing rate, and 7 out of 10 cases for CS600ms pacing rate. For the simulations where replacement fibrosis was included (percolation), both the roof and anterior wall exhibited the largest degree of rotational activity in 5 out of 10 cases at CS250ms pacing rate; while at CS600ms pacing rate, the posterior walled exhibited the majority of rotational activity in 7 cases (Figure B). All fibrosis models containing ionic changes terminated in more than half of the 10 cases for both pacing rates (CS250ms and 600). Conclusion The impact of low voltage on fibrotic remodelling causes complex effects on AF dynamics.

Structure of an internal loop motif with three consecutive U•U mismatches from stem-loop 1 in the 3'-UTR of the SARS-CoV-2 genomic RNA.

The single-stranded RNA genome of SARS-CoV-2 is highly structured. Numerous helical stem-loop structures interrupted by mismatch motifs are present in the functionally important 5'- and 3'-UTRs. These mismatches modulate local helical geometries and feature unusual arrays of hydrogen bonding donor and acceptor groups. However, their conformational and dynamical properties cannot be directly inferred from chemical probing and are difficult to predict theoretically. A mismatch motif (SL1-motif) consisting of three consecutive U•U base pairs is located in stem-loop 1 of the 3'-UTR. We combined NMR-spectroscopy and MD-simulations to investigate its structure and dynamics. All three U•U base pairs feature two direct hydrogen bonds and are as stable as Watson-Crick A:U base pairs. Plasmodium falciparum 25S rRNA contains a triple U•U mismatch motif (Pf-motif) differing from SL1-motif only with respect to the orientation of the two closing base pairs. Interestingly, while the geometry of the outer two U•U mismatches was identical in both motifs the preferred orientation of the central U•U mismatch was different. MD simulations and potassium ion titrations revealed that the potassium ion-binding mode to the major groove is connected to the different preferred geometries of the central base pair in the two motifs.