Computational Model Research Articles

Expanding the therapeutic window of gramicidin S towards a safe and effective systemic treatment of methicillin-resistant S. aureus infections

The rise of multidrug-resistant bacteria, such as Methicillin-resistant Staphylococcus aureus (MRSA), necessitates the development of new antibacterial therapies. Antimicrobial peptides offer a promising alternative to conventional antibiotics due to their unique mechanisms of action. Gramicidin S exhibits potent bactericidal activity against S. aureus, however, high haemolytic toxicity currently limits its application to topical use. A new series of gramicidin S analogues is presented with rational modifications to the β-turn and β-strand regions, to reduce haemolytic and nephrotoxic effects, while preserving antibacterial potency. The minimum inhibitory concentration (MIC) for each analogue was determined against benchmark methicillin-sensitive S. aureus (MSSA) and MRSA clinical isolates, with toxicity characterised in vitro using human red blood cells and human embryonic kidney cells (HEK-293). Peptide 12 demonstrated a significant two-fold increase in antibacterial activity against both MSSA and MRSA (MIC: 2 μg/mL) compared to gramicidin S (MIC: 4 μg/mL), albeit with increased cytotoxicity. Similarly, peptide 15 showed exceptional efficacy (MIC: 3 μg/mL), but with reduced cytotoxicity, culminating in a two-fold improvement to the therapeutic index (TI) of gramicidin S. Peptides 14 (HC50: 50.48 ± 1.15 μg/mL, IC50: 38.09 μg/mL) and 16 (HC50: 84.09 ± 1.02 μg/mL, IC50: 12.60 μg/mL) also significantly reduced haemolytic toxicity and nephrotoxicity, compared to gramicidin S (HC50: 12.34 ± 0.27 μg/mL, IC50: 6.45 μg/mL). Detailed NMR, CD and computational modelling were used to provide critical insights into how molecular conformation influences both antibacterial potency and cytotoxicity. Collectively, these results expand the therapeutic window of gramicidin S by up to 12-fold, with negligible cytotoxicity observed at concentrations well beyond the acceptable safety threshold, which indicates the potential for safe systemic administration in the treatment of infection caused by resistant pathogens.

Sparsity of Population Activity in the Hippocampus Is Task‐Invariant Across the Trisynaptic Circuit and Dorsoventral Axis

ABSTRACTEvidence from neurophysiological and genetic studies demonstrates that activity sparsity—the proportion of neurons that are active at a given time in a population—systematically varies across the canonical trisynaptic circuit of the hippocampus. Recent work has also shown that sparsity varies across the hippocampal dorsoventral (long) axis, wherein activity is sparser in ventral than dorsal regions. While the hippocampus has a critical role in long‐term memory (LTM), whether sparsity across the trisynaptic circuit and hippocampal long axis is task‐dependent or invariant remains unknown. Importantly, representational sparsity has significant implications for neural computation and theoretical models of learning and memory within and beyond the hippocampus. Here we used functional molecular imaging to quantify sparsity in the rat hippocampus during performance of the Morris water task (MWT) and contextual fear discrimination (CFD) – two popular and distinct assays of LTM. We found that activity sparsity is highly reliable across memory tasks, wherein activity increases sequentially across the trisynaptic circuit (DG < CA3 < CA1) and decreases across the long axis (ventral<dorsal). These results have important implications for models of hippocampal function and suggest that activity sparsity is a preserved property in the hippocampal system across cognitive settings.

Correction: Analyzing Urban Expansion and Land Use Dynamics in Bagua Grande and Chachapoyas Using Cloud Computing and Predictive Modeling

Correction: Analyzing Urban Expansion and Land Use Dynamics in Bagua Grande and Chachapoyas Using Cloud Computing and Predictive Modeling

Characterization of Guest DNA Transport and Adsorption within Host Porous Protein Crystals.

Nucleic acid transport through protein-based pores is a well-characterized phenomenon due in part to advancements in nanopore sequencing. A less studied area is nucleic acid transport through extended protein-based channels, where the additional surface area and increased contact time allow for the study of prolonged binding interactions. Porous protein crystals composed of "CJ", a putative polyisoprenoid-binding protein from Campylobacter jejuni, represent a favorable, highly ordered material for studying DNA transport and binding/unbinding along protein-based channels. These crystals adopt a hexagonal prism habit and contain a densely packed hexagonal array of 13 nm diameter axial nanopores that run from the top to the bottom of the crystal. After cross-linking, the crystals are easily manipulated for experimentation. An adsorption isotherm between host crystals and guest double-stranded 8 base pair DNA (8mer) revealed a high equilibrium adsorption constant of 206 ± 30 L/g. Fluorescence confocal microscopy tracked the loading of guest DNA into host crystals predominately along the major axial crystal nanopores. Four different computational models based on the finite volume (FV) method were assessed to model the transport process for guest 8mer and 15mer dsDNA loading into empty host crystals in terms of fundamental parameters, such as the intrapore diffusion constant. Fitting the models to the data revealed that the most basic FV model sufficed to describe the observed loading behavior, characterized by a single effective diffusion coefficient. Leveraging Fick's first law, we more directly fit a numerical range for the observed intrapore diffusion coefficient as a function of time, position within the crystal, and relative guest concentration. This new transport analysis strategy was applied to both out-of-equilibrium loading and fluorescence recovery after photobleaching (FRAP) experiments. The intrapore diffusion constants are comparable between 8mer and 15mer dsDNA and were found to be 2 orders of magnitude faster for DNA loading into empty crystals than that observed in FRAP experiments, which averaged (10 ± 4) × 10-11 cm2/s.

Integrating expression profiling and computational modeling to elucidate PTEN‑miR‑141 interactions in oral squamous cell carcinoma

Integrating expression profiling and computational modeling to elucidate PTEN‑miR‑141 interactions in oral squamous cell carcinoma

A quantum leaky integrate-and-fire spiking neuron and network

Quantum machine learning is in a period of rapid development and discovery, however it still lacks the resources and diversity of computational models of its classical complement. With the growing difficulties of classical models requiring extreme hardware and power solutions, and quantum models being limited by noisy intermediate-scale quantum (NISQ) hardware, there is an emerging opportunity to solve both problems together. Here we introduce a new software model for quantum neuromorphic computing — a quantum leaky integrate-and-fire (QLIF) neuron, implemented as a compact high-fidelity quantum circuit, requiring only 2 rotation gates and no CNOT gates. We use these neurons as building blocks in the construction of a quantum spiking neural network (QSNN), and a quantum spiking convolutional neural network (QSCNN), as the first of their kind. We apply these models to the MNIST, Fashion-MNIST, and KMNIST datasets for a full comparison with other classical and quantum models. We find that the proposed models perform competitively, with comparative accuracy, with efficient scaling and fast computation in classical simulation as well as on quantum devices.

Neural network-based arterial diameter estimation from ultrasound data

Cardiovascular diseases are the leading cause of mortality and early assessment of carotid artery abnormalities with ultrasound is key for effective prevention. Obtaining the carotid diameter waveform is essential for hemodynamic parameter extraction. However, since it is not a trivial task to automate, compact computational models are needed to operate reliably in view of physiological variability. Modern machine learning (ML) techniques hold promise for fully automated carotid diameter extraction from ultrasonic data without requiring annotation by trained clinicians. Using a conventional digital signal processing (DSP) based approach as reference, our goal is to (a) build data-driven ML models to identify and track the carotid diameter, and (b) keep the computational complexity minimal for deployment in embedded systems. A ML pipeline is developed to estimate the carotid artery diameter from Hilbert-transformed ultrasound signals acquired at 500Hz sampling frequency. The proposed ML pipeline consists of 3 processing stages: two neural-network (NN) models and a smoothing filter. The first NN, a compact 3-layer convolutional NN (CNN), is a region-of-interest (ROI) detector confining the tracking to a reduced portion of the ultrasound signal. The second NN, an 8-layer (5 convolutional, 3 fully-connected) CNN, tracks the arterial diameter. It is followed by a smoothing filter for removing any superimposed artifacts. Data was acquired from 6 subjects (4 male, 2 female, 37 ± 7 years, baseline mean arterial pressure 86.3 ± 7.6 mmHg) at rest and with diameter variation induced by paced breathing and a hand grip intervention. The label reference is extracted from a fine-tuned DSP-based approach. After training, diameter waveforms are extracted and compared to the DSP reference. The predicted diameter waveform from the proposed NN-based pipeline has near perfect temporal alignment with the reference signal and does not suffer from drift. Specifically, we obtain a Pearson correlation coefficient of r = 0.87 between prediction and reference waveforms. The mean absolute deviation of the arterial diameter prediction was quantified as 0.077 mm, corresponding to a 1% error given an average carotid artery diameter of 7.5 mm in the study population. This work proposed and evaluated an ML neural network-based pipeline to track the carotid artery diameter from an ultrasound stream of A-mode frames. By contrast to current clinical practice, the proposed solution does not rely on specialist intervention (e.g. imaging markers) to track the arterial diameter. In contrast to conventional DSP-based counterpart solutions, the ML-based approach does not require handcrafted heuristics and manual fine-tuning to produce reliable estimates. Being trainable from small cohort data and reasonably fast, it is useful for quick deployment and easy to adjust accounting for demographic variability. Finally, its reliance on A-mode ultrasound frames renders the solution promising for miniaturization and deployment in on-line clinical and ambulatory monitoring.

Robotics at the University of Pennsylvania from Birth to Maturity: A Review of 30 Years of Research

This article is an attempt to reexamine the evolution of robotics research at the University of Pennsylvania and all that it entailed, covering the successes and struggles of PhD students, postdoctoral researchers, and a few dedicated faculty between 1972 and 2000. In 1945, Penn's Moore School of Electrical Engineering was famous for developing the first electronic digital computer, ENIAC, but after a few years, the research had diminished. In 1972, a new Department of Computer and Information Science was formed. I came to Penn from Stanford University's Artificial Intelligence Laboratory full of energy, enthusiasm, and the goal of establishing a similar lab at Penn. This article demonstrates the creativity and ingenuity of young professionals at the General Robotics, Automation, Sensing, and Perception (GRASP) Laboratory. We built hardware. We built software. We collaborated with psychologists and electrical and mechanical engineers and tried to build a community of roboticists. Our curiosity led us to build new vision and tactile systems and to investigate cooperative robotics systems on the ground and in the air. We used computational models supported and verified by experiments. Today, I am proud to say that almost all of the students who cycled through the GRASP Lab are successful in academia or industry.

Uncertainty Modeling and Stability Assessment of Minimum Spanning Trees in Network Design

The Minimum Spanning Tree (MST) problem in networks focuses on finding efficient routes, with applications in transportation, logistics, telecommunications, and more. However, catastrophes can make these networks uncertain, requiring robust computational models for decision-making. This paper introduces an uncertainty theory-based model to analyze the stability of MSTs in uncertain networks. By incorporating reliability and risk variables, we assess the robustness of uncertain MSTs (UMSTs) and address the challenge of computing link tolerances, which define the range within which network links can vary without compromising MST optimality. This study proposes computational formulations to systematically calculate these tolerances, offering a more efficient alternative to traditional re-optimization methods.

Computational analysis of hydrogen bubble formation and dynamics in electrolytic systems using COMSOL

This study explores the numerical modeling of hydrogen bubble dynamics in electrolytic processes, utilizing COMSOL Multiphysics software. The focus is on the development of precise computational models to simulate the processes of bubble formation, growth, and movement in water electrolysis systems, which are crucial for optimizing hydrogen production. Using 2D axisymmetric modeling, the research applies several interface-capturing techniques, including phase field, level set, and moving mesh methods, to accurately capture the behavior of hydrogen bubbles in various operational conditions. By analyzing these dynamics, the study aims to improve the understanding of bubble-related phenomena in electrolysis, such as formation patterns, bubble size, and the terminal velocities of rising hydrogen bubbles. Additionally, the effects of density differences between hydrogen and water are examined to assess their impact on the overall efficiency of electrolysis. The results indicate that the moving mesh method offers the best performance in accurately modeling bubble dynamics, providing insights that can contribute to the optimization of electrolysis processes for efficient hydrogen production.

Considerations for Use of Three-Hole Probes for Turbomachinery Applications

Three-hole probes are frequently employed to characterize the flow field upstream, within, and downstream of turbomachines. From three pressure measurements, the static pressure, total pressure, Mach number, and a single axis flow angle can be resolved. Despite this utility, there are induced errors related to the use of these probes that are particularly pertinent to use in turbomachines. Small passages cause end-wall proximity effects, the rotor induces flow unsteadiness, and complex flow three-dimensional flow structures are developed. These effects are considered in this effort by contrasting experimental inlet distortion data from an auxiliary power unit inlet of a centrifugal compressor with computational inlet-only models. The aerodynamic interface plane was measured using a rotatable rake assembly with trapezoidal three-hole probes and fast response pressure transducers. Two computational models are presented, one with and one without the aerodynamic interface plane instrumentation used in the experiment. The measurements from the simulated traverse are compared to the inlet-only computational model and experimental results. End-wall effects were the dominant error driver for the static pressure measurements. Swirl was predominantly altered by perpendicular flow angle magnitudes above 10 deg. Indicated total pressure was impacted by radial flow angle and unsteadiness that were highly coupled.

Assessing flow diverter porosity: a comparative analysis of quantification techniques based on imaging and simulation

ABSTRACT Background/Purpose Flow diverter porosity directly influences the blood flow reduction at the aneurysm neck level and the anatomical result of the treatment. In this research, we present and compare three methodologies to determine the local porosity of deployed flow diverters. Method Three-dimensional rotational angiography was used to obtain computational vessel models of three patients. Different flow diverters were virtually deployed in the computational models and implanted in 3D-printed models of the vasculatures by interventional neuroradiologists. Experimental porosity determinations were conducted using 2D microscope photographs and 3D Dyna-CT images (i.e. cone-beam Computed Tomography angiographic images), while simulated porosity was computed using ANKYRAS software. Results No statistically significant differences were observed between the porosity distributions from the three methods (p > 0.01). When computing the differences point-by-point, narrow distributions centered on zero were obtained, revealing a good agreement in the determinations. Orthogonal regression analysis affirmed this equivalence. The lowest agreement between porosity measurement methods was observed to occur at curve segments with relatively low porosity. Conclusions The local porosity of deployed flow diverters can be accurately determined by the three methods presented in this work. Assessing FD porosity with 3D Dyna-CT images would allow the evaluation of real patient data, whereas simulations could determine local porosity before the treatment.

A computational fluid dynamics-based girth welding model for large-diameter pipeline to reveal vertical weld formation mechanism

Welding quality significantly impacts the safety of large-diameter, long-distance pipelines. The molten pool flow, influenced by gravity, results in variations in the girth weld seam at different circumferential orientations. A three-dimensional transient computational fluid dynamics model was developed to study the multi-layer, multi-pass welding process of X80M pipeline, incorporating arc heat, droplet heat, gravity, and fluid flow in the molten pool. The model also simulates dual-torch automatic welding to examine heat transfer and flow characteristics. Notably, it reveals the morphological evolution of the girth weld seam from the 2 to 3 o'clock positions during continuous welding, an area lacking prior research. The results, validated by experiments, show good agreement between the simulation and thermal cycle curves. The weld pool solidifies from the sides toward the center, creating a concave shape due to downward metal flow, with dual-torch welding intensifying this effect. The second torch increases the concavity of filler layers, with depths rising by 21.81% and 32.20%. These findings offer new insights into pipeline girth welding.

Unraveling the mechanism: How does β-phosphoglucomutase from the haloacid dehalogenase superfamily catalyze the interconversion of β-d-glucose 1-phosphate and β-d-glucose 6-phosphate? A chemical perspective

The catalytic mechanisms of enzymes can be phylogenetically mapped corresponding to their catalytic structures. This mapping effectively elucidates the diversity of enzyme catalytic mechanisms and the emergence of new enzymatic activities within enzyme superfamilies. The haloacid dehalogenase (HAD) superfamily serves as an exemplary model system for comprehending the co-evolution of catalytic structures and mechanisms. This study delves into the mechanism underlying the functional divergence of β-phosphoglucomutase (β-PGM) from the phosphatase branch of the HAD superfamily, employing a chemical perspective. Through the construction and calculation of three models of varying scales using the Density Functional Theory method with B3LYP function, we aim to investigate the chemical mechanism driving this functional divergence of β-PGM from the HAD family. The computational results indicate that residues His20 and Lys76 in the second shell stabilize substrates and enhance the acid-base catalytic ability of Asp10. Additionally, residues Arg49, Ser116 and Asn118 facilitate substrate binding by engaging in close hydrogen bonding interactions with the substrates. Through cooperative action, these residues enable β-PGM to function as an efficient phosphoglucomutase. Through computational modeling and a chemical perspective, we unravel the mechanisms enabling β-PGM to convert β-d-glucose 1-phosphate to β-d-glucose 6-phosphate. Finally, based on the analysis of the evolutionary tree, we discussed and summarized the evolutionary relationships among different forms of metal cores of hydrolases.

Impact of vascular Ehlers-Danlos Syndrome-associated Gly substitutions on structure, function, and mechanics using bacterial collagen.

Vascular Ehlers-Danlos syndrome (vEDS) arises from mutations in collagen-III, a major structural component of the extracellular matrix (ECM) in vascularized tissues, including blood vessels. Fibrillar collagens form a triple-helix that is characterized by a canonical (Gly-X-Y)n sequence. The substitution of another amino acid for Gly within this conserved repeating sequence is associated with several hereditary connective tissue disorders, including vEDS. The clinical severity of vEDS depends on the identity of the substituted amino acid and its location. In this study, we engineered recombinant bacterial collagen-like proteins (CLPs) with previously reported Gly→X (X=Ser or Arg) vEDS substitutions within the integrin-binding site. Employing a combination of biophysical techniques, enzymatic digestion assays, integrin binding affinity assays, and computational modeling, we assessed the impact of Gly→X substitutions on structure, stability, function, and mechanical properties. While constructs with Ser or Arg substitutions maintained a triple-helix structure, Arg substitution significantly reduced global thermal stability, heightened susceptibility to trypsin digestion, and altered integrin α2-inserted (α2I) domain binding. Molecular dynamics (MD) simulations also demonstrated distinct effects of different Gly substitutions on the triple-helix structure - Arg substitutions induced notable bulging at the substitution site and disrupted interchain hydrogen bonds compared to Ser substitutions. Additionally, steered MD simulations revealed that Arg substitution led to a significant decrease in Young's modulus of the triple-helix. Bacterial CLPs have proved to be a powerful model for studying the underlying mechanisms of vEDS-causing mutations in collagen-III. Serine and arginine substitutions differentially perturb cell-matrix interactions and ECM in a manner consistent with clinical vEDS severity.

A multi-model approach identifies ALW-II-41-27 as a promising therapy for osteoarthritis-associated inflammation and endochondral ossification

Low-grade inflammation and pathological endochondral ossification are key processes underlying the progression of osteoarthritis, the most prevalent joint disease worldwide. In this study, we employed a multi-faceted approach, integrating publicly available datasets, in silico analyses, in vitro experiments and in vivo models to identify new therapeutic candidates targeting these processes. Data mining of transcriptomic datasets identified EPHA2, a receptor tyrosine kinase associated with cancer, as being linked to both inflammation and endochondral ossification in osteoarthritis. A computational model of cellular signaling networks in chondrocytes predicted that in silico activation of EPHA2 in healthy chondrocytes increases inflammatory mediators and induces hypertrophic differentiation, a hallmark of endochondral ossification. We then evaluated the effect of EPHA2 inhibition using the tyrosine kinase inhibitor ALW-II-41-27 in cultured human chondrocytes from individuals with osteoarthritis, demonstrating significant reductions in both inflammation and hypertrophy. Additionally, systemic subcutaneous administration of ALW-II-41-27 in a mouse osteoarthritic model attenuated joint degeneration by reducing local inflammation and pathological endochondral ossification. Collectively, this study demonstrates a novel drug discovery pipeline that integrates computational, experimental, and animal models, paving the way for the development of disease-modifying treatments for osteoarthritis.

Task and Timing Effects in Argument Role Sensitivity: Evidence From Production, EEG, and Computational Modeling.

Comprehenders generate expectations about upcoming lexical items in language processing using various types of contextual information. However, a number of studies have shown that argument roles do not impact neural and behavioral prediction measures. Despite these robust findings, some prior studies have suggested that lexical prediction might be sensitive to argument roles in production tasks such as the cloze task or in comprehension tasks when additional time is available for prediction. This study demonstrates that both the task and additional time for prediction independently influence lexical prediction using argument roles, via evidence from closely matched electroencephalogram (EEG) and speeded cloze experiments. In order to investigate the timing effect, our EEG experiment used maximally simple Japanese stimuli such as Bee-nom/acc sting, and it manipulated the time for prediction by changing the temporal interval between the context noun and the target verb without adding any further linguistic content. In order to investigate the task effect, we conducted a speeded cloze study that was matched with our EEG study both in terms of stimuli and the time available for prediction. We found that both the EEG study with additional time for prediction and the speeded cloze study with matched timing showed clear sensitivity to argument roles, while the EEG conditions with less time for prediction replicated the standard pattern of argument role insensitivity. Based on these findings, we propose that lexical prediction is initially insensitive to argument roles but a monitoring mechanism serially inhibits role-inappropriate candidates. This monitoring process operates quickly in production tasks, where it is important to quickly select a single candidate to produce, whereas it may operate more slowly in comprehension tasks, where multiple candidates can be maintained until a continuation is perceived. Computational simulations demonstrate that this mechanism can successfully explain the task and timing effects observed in our experiments.

Review of cloud computing models in education and the unmet needs

<p>This article thoroughly examines recently proposed cloud computing (CC) models used within the higher educational institutions (HEI) field, scrutinizing their objectives, structures, and incorporated requirements. Each model's unique architecture and functionality are analyzed to understand their potential educational contributions. Beyond technical considerations, the study explores nuanced requirements essential for successful integration in educational settings. The review exposes diverse aims pursued by the models, such as enhanced scalability, collaborative learning, and resource management, emphasizing their capacity to reshape traditional educational paradigms. However, a notable gap emerges-the absence of cultural and requirement elicitation models within the frameworks. Despite growing cultural diversity and varied educational needs, most models lack components addressing cultural nuances and robust requirement elicitation. In conclusion, the paper identifies a pressing need for a transformative shift in developing CC models for education. The absence of dedicated cultural and requirement elicitation models challenges the holistic effectiveness of these frameworks. Future efforts should prioritize integrating culturally sensitive components and comprehensive requirement elicitation strategies to create adaptive, universally applicable, and inclusive CC educational environments. Addressing these gaps will pave the way for a nuanced and responsive integration of CC technologies in diverse educational settings.</p>

ЧИСЛЕННОЕ МОДЕЛИРОВАНИЕ ГИДРОДИНАМИЧЕСКИХ ПРОЦЕССОВ В ГЕРОТОРНОМ НАСОСЕ СИСТЕМЫ СМАЗКИ ДИЗЕЛЬНЫХ ДВИГАТЕЛЕЙ

This article presents the results that serve as a methodological basis for creating a digital twin of a diesel engine lubrication system. At the first stage, mathematical and computer models have been created for the oil pump of the engine lubrication system. At the second stage, numerical modeling of hydrodynamic processes during pump operation has been carried out in order to verify and validate models based on experimental data. Next, a method of reverse engineering of pump performance characteristics is proposed, and its performance characteristic is constructed. Based on the calculations performed, recommendations have been developed to improve the accuracy of constructing mathematical and computer models of digital twins of a gerotor oil pump. Calculations were carried out for the pump with a modified design.

ESM-scan-A tool to guide amino acid substitutions.

Protein structure prediction and (re)design have gone through a revolution in the last 3 years. The tremendous progress in these fields has been almost exclusively driven by readily available machine learning algorithms applied to protein folding and sequence design problems. Despite these advancements, predicting site-specific mutational effects on protein stability and function remains an unsolved problem. This is a persistent challenge, mainly because the free energy of large systems is very difficult to compute with absolute accuracy and subtle changes to protein structures are hard to capture with computational models. Here, we describe the implementation and use of ESM-Scan, which uses the ESM zero-shot predictor to scan entire protein sequences for preferential amino acid changes, thus enabling in silico deep mutational scanning experiments. We benchmark ESM-Scan on its predictive capabilities for stability and functionality of sequence changes using three publicly available datasets and proceed by experimentally testing the tool's performance on a challenging test case of a blue-light-activated diguanylate cyclase from Methylotenera species (MsLadC), where it accurately predicted the importance of a highly conserved residue in a region involved in allosteric product inhibition. Our experimental results show that the ESM-zero shot model is capable of inferring the effects of a set of amino acid substitutions in their correlation between predicted fitness and experimental results. ESM-Scan is publicly available at https://huggingface.co/spaces/thaidaev/zsp.