Matteo CeriottiInteractive 3-Body Problem
https://trisolarchaos.com/
Explore the famous three-body problem with this interactive N-body #physics simulator. Real-time 3D visualization of gravitational #dynamics and #orbital #mechanics.
Linux Audio DevelopmentAnother new #interview with a #linuxaudio #developer has been published! ๐ค
This time with the creator of the popular #loudness and #dynamics #measurement #plugin Multimeter, Samuel Gรคhwiler from Klangfreund.
Among many other things, we discussed accessibility features in plugin development.
https://linuxaudio.dev/linux-audio-developers-spotlight/klangfreund
Rxiv mechanobio๐ฐ "Chlamydia trachomatis highjacks host MYO1C for actin cage recruitment at the bacterial inclusion"
https://doi.org/doi:10.1016/j.micres.2025.128395
https://pubmed.ncbi.nlm.nih.gov/41242206/
#Dynamics #Actin
https://doi.org/doi:10.1016/j.micres.2025.128395
https://pubmed.ncbi.nlm.nih.gov/41242206/
#Dynamics #Actin
๐ฐ "Optimization of Argon Plasma Working Pressure through Parallel PIC Simulations for Enhancement of Material Surface Treatment"
https://arxiv.org/abs/2511.11145 #Physics.Plasm-Ph #Dynamics #Cell
https://arxiv.org/abs/2511.11145 #Physics.Plasm-Ph #Dynamics #Cell
Optimization of Argon Plasma Working Pressure through Parallel PIC Simulations for Enhancement of Material Surface Treatment
In this study, a novel method for simulating plasma dynamics using parallel programming has been developed. The equations based on Particle-in-Cell (PIC) method were utilized and adapted for this purpose. We utilized 35 processors from Sharif High Performance Computing (HPC) center and divided the plasma volume into 35 parts, with each part's PIC equation solved on a separate processor. Once the computations were completed, the results from all processors were combined to form a complete plasma volume. The simulations revealed that there is an optimal pressure for argon, at which the ion flux onto the electrode surface is maximized. Increasing the absolute value of the electrode potential also increases this flux. Therefore, for a given potential, selecting the optimal pressure is crucial for the most effective surface modification using argon plasma. In this work, for applied voltage of -500 V, the optimum pressure was 100 mTorr.
๐ฐ "Deep Learning-Enhanced Analysis for Delineating Anticoagulant Essay Efficacy Using Phase Microscopy"
https://arxiv.org/abs/2511.11158 #Physics.Optics #Dynamics #Cs.Cv #Cell
https://arxiv.org/abs/2511.11158 #Physics.Optics #Dynamics #Cs.Cv #Cell
Deep Learning-Enhanced Analysis for Delineating Anticoagulant Essay Efficacy Using Phase Microscopy
The coagulation of blood after it is drawn from the body poses a significant challenge for hematological analysis, potentially leading to inaccurate test results and altered cellular characteristics, compromising diagnostic reliability. This paper presents a deep learning-enhanced framework for delineating anticoagulant efficacy ex vivo using Digital Holographic Microscopy (DHM). We demonstrate a label-free, non-invasive approach for analyzing human blood samples, capable of accurate cell counting and morphological estimation. A DHM with an automated image processing and deep learning pipeline is built for morphological analysis of the blood cells under two different anti-coagulation agents, e.g. conventional EDTA and novel potassium ferric oxalate nanoparticles (KFeOx-NPs). This enables automated high-throughput screening of cells and estimation of blood coagulation rates when samples are treated with different anticoagulants. Results indicated that KFeOx-NPs prevented human blood coagulation without altering the cellular morphology of red blood cells (RBCs), whereas EDTA incubation caused notable changes within 6 hours of incubation. The system allows for quantitative analysis of coagulation dynamics by assessing parameters like cell clustering and morphology over time in these prepared samples, offering insights into the comparative efficacy and effects of anticoagulants outside the body.
๐ฐ "Stochastic Kinetics of mRNA Molecules in a General Transcription Model"
https://arxiv.org/abs/2502.08028 #Physics.Bio-Ph #Dynamics #Matrix
https://arxiv.org/abs/2502.08028 #Physics.Bio-Ph #Dynamics #Matrix
Stochastic Kinetics of mRNA Molecules in a General Transcription Model
Stochastic modeling of transcription is a classic yet long-standing problem in theoretical biophysics. The lack of unified results and a computationally efficient approach for a general, fine-grained transcription model has confined relevant research to some over-simplified special cases like the Telegraph model. This article establishes a general, unified and computationally efficient framework for studying stochastic transcription kinetics. We consider a chemical reaction model of transcription and construct the time-dependent solution to the corresponding chemical master equation. A well-known matrix-form expression for steady-state binomial moments is recovered by calculating the temporal limit of the time-dependent dynamics. Two novel inequalities for binomial moments and the probability mass function are derived using techniques from functional analysis. It follows that the distribution of mRNA counts is upper-bounded by a constant multiple of Poisson distribution, thus mathematically proving the main statement of the Heavy-Tailed Law. Additionally, the standard binomial moment method is analyzed from a numerical perspective, where truncation error is estimated using our inequalities. Compared with some widely-used numerical methods, a key advantage of this result is the significantly lower computational complexity.
๐ฐ "Viral population dynamics at the cellular level, considering the replication cycle"
https://arxiv.org/abs/2510.14481 #Q-Bio.Qm #Q-Bio.Pe #Dynamics #Cell
https://arxiv.org/abs/2510.14481 #Q-Bio.Qm #Q-Bio.Pe #Dynamics #Cell
Viral population dynamics at the cellular level, considering the replication cycle
Viruses are microscopic infectious agents that require a host cell for replication. Viral replication occurs in several stages, and the completion time for each stage varies due to differences in the cellular environment. Thus, the time to complete each stage in viral replication is a random variable. However, no analytic expression exists for the viral population at the cellular level when the completion time for each process constituting viral replication is a random variable. This paper presents a simplified model of viral replication, treating each stage as a renewal process with independently and identically distributed completion times. Using the proposed model, we derive an analytical formula for viral populations at the cellular level, based on viewing viral replication as a birth-death process. The mean viral count is expressed via probability density functions representing the completion time for each step in the replication process. This work validates the results with stochastic simulations. This study provides a new quantitative framework for understanding viral infection dynamics.
๐ฐ "MLT-11 is necessary for C. elegans embryogenesis and conserved sequences play distinct roles in cuticle structure"
https://www.biorxiv.org/content/10.1101/2025.11.14.688552v1?rss=1 #Extracellular #Dynamics
https://www.biorxiv.org/content/10.1101/2025.11.14.688552v1?rss=1 #Extracellular #Dynamics
๐ฐ "Conformational dynamics of actin filaments crosslinked with alpha-actinin and their roles in suppressing cofilin-induced helical shortening and cluster formation"
https://www.biorxiv.org/content/10.1101/2025.11.16.688667v1?rss=1 #Morphogenesis #Dynamics #Actin
https://www.biorxiv.org/content/10.1101/2025.11.16.688667v1?rss=1 #Morphogenesis #Dynamics #Actin
๐ฐ "PAF15 stabilizes PCNA on DNA and slows down its sliding dynamics"
https://www.biorxiv.org/content/10.1101/2025.11.15.688598v1?rss=1 #Dynamics #Cell
https://www.biorxiv.org/content/10.1101/2025.11.15.688598v1?rss=1 #Dynamics #Cell