Inbred mouse strains provide phenotypic homogeneity between individual mice. However, stochastic morphogenetic events combined with epigenetic changes due to exposure to environmental factors and ontogenic experience result in variability among mice with virtually identical genotypes, reducing the reproducibility of experimental mouse models. Here we used microscopic and cytometric techniques to identify individual patterns in gut-associated lymphoid tissue (GALT) that are induced by exposure to microbiota. By comparing germ-free (GF), conventional (CV) and gnotobiotic mice colonized with a defined minimal mouse microbiota (oMM12) MHC II-EGFP knock-in mice we quantified antigen-presenting cells (APCs) in the lamina propria, cryptopatches (CP), isolated lymphoid follicles (ILFs), Peyer's patches (PPs) and specific sections of the mesenteric lymphoid complex. We found that GF mice had a significantly larger outer intestinal surface area compared to CV and oMM12-colonized mice, which partially compensated for their lower density of the villi in the distal ileum. GF mice also contained fewer APCs than oMM12 mice in the Iamina propria of the villi and had a significantly smaller volume of the solitary intestinal lymphoid tissue (SILT). In both GF and oMM12 mice, PP follicles were significantly smaller compared to CV mice, although number was similar. Concomitantly, the number of pDCs in PPs was significantly lower in GF mice than in CV mice. Moreover, the cecal patch was dispersed into small units in GF mice whereas it was compact in CV mice. Taken together, we here provide further evidence that microbiota regulates SILT differentiation, the size and morphology of PPs, the cellular composition of mesenteric lymph nodes (MLNs) and the morphology of cecal patch. As such, microbiota directly affect not only the functional configuration of the immune system but also the differentiation of lymphoid structures. These findings highlight how standardized microbiota, such as oMM12, can promote reproducibility in animal studies by enabling microbiologically controlled experiments across laboratories.
A fundamental approach to understanding chemical processes involves two key concepts: reaction paths and vibrational wavepackets. Collecting sufficient observables to experimentally determine these paths still challenges the latest advances in ultrafast science. Simultaneously observing the coherent nature of the wavepacket following them is even more challenging. Here, exploiting the sub-femtosecond time resolution (σ = 1 fs) of attosecond soft-X-ray-absorption spectroscopy, we overcome both of these challenges and observe a Jahn-Teller-mediated chemical reaction in its entirety-from initial symmetry breaking to beyond dissociation. We find that the Jahn-Teller effect in SiH 4 + immediately bifurcates the reaction into two channels: ballistic dissociation into SiH 3 + and H in 22.9 ± 0.5 fs in which the vibrational wavepacket is preserved, and-after an induction time of 11 ± 3.4 fs-a stochastic dissociation into SiH 2 + and H2 with a timescale of 140 ± 19 fs in which the wavepacket dephases. We find that adiabatic ab-initio molecular dynamics simulations correctly reproduce the ballistic channel, but fail with the stochastic channel. These unprecedented insights into an ultrafast Jahn-Teller-mediated chemical reaction establish the unique potential of our experimental scheme for investigating chemical processes, particularly ones containing non-adiabatic dynamics or involving hydrogen atoms, which are notoriously difficult to detect with other methods, such as electron or X-ray diffraction.
- Publication type
- Journal Article MeSH
This study presents a comprehensive analytical framework for modeling electric vehicle (EV) charging infrastructures through a stochastic queueing-theoretic approach that explicitly incorporates critical customer behavioral dynamics. The proposed model addresses key phenomena often overlooked in classical frameworks, including customer impatience (reneging), balking behavior, feedback mechanisms, and state-dependent service threshold policies, within a finite-population, multiple-server environment. These behavioral elements reflect realistic operational scenarios in which users may opt not to join extended queues, abandon the system due to excessive delays, or return for service completion based on prior dissatisfaction. The system dynamics are formulated using a continuous-time Markov chain (CTMC), and the corresponding Chapman-Kolmogorov differential equations are derived to characterize state transitions. Employing a matrix-analytic solution technique, the steady-state probability distribution is obtained, enabling the computation of multiple performance metrics such as system occupancy, server utilization, abandonment rates, and throughput. Numerical simulations validate the model's applicability and highlight intricate interdependencies among customer tolerance thresholds, service quality levels, and operational performance indicators. The findings offer valuable insights into capacity planning, congestion control, and service optimization, providing a rigorous decision-support framework for the design and management of EV charging networks under uncertain and dynamic user behavior. The study also outlines practical managerial implications and suggests directions for future research to enhance the adaptability and efficiency of smart charging infrastructures.
The research focuses on optical solitons and employs the generalized auxiliary equation technique to obtain soliton resolutions for the nonlinear Kairat-X equation. This equation considers wave number groups influenced by time and velocity dispersion in non-linear mediums. Because of their stability and numerous uses in signal processing, telecommunications, and quantum physics, optical solitons are appreciated. Novel periodic, exponential, and other soliton solutions are shown in the work, and the dynamics of the model are thoroughly examined using phase portraits, quasi-periodic patterns, Lyapunov exponents, 3D attractors, 2D power spectra, and sensitivity analysis. Various simulations show how noise intensity variations affect system sensitivity and instability through the assessment of stochastic sensitivity along with Poincaré, and Lyapunov analysis. These results provide a significant addition to the discipline.
- Keywords
- Chaos, Lyapunov exponent, Multistability, Sensitivity analysis,
- Publication type
- Journal Article MeSH
Moran Birth-death process is a standard stochastic process that is used to model natural selection in spatially structured populations. A newly occurring mutation that invades a population of residents can either fixate on the whole population or it can go extinct due to random drift. The duration of the process depends not only on the total population size n, but also on the spatial structure of the population. In this work, we consider the Moran process with a single type of individuals who invade and colonize an otherwise empty environment. Mathematically, this corresponds to the setting where the residents have zero reproduction rate, thus they never reproduce. The spatial structure is represented by a graph. We present two main contributions. First, in contrast to the Moran process in which residents do reproduce, we show that the colonization time is always at most a polynomial function of the population size n. Namely, we show that colonization always takes at most [Formula: see text] expected steps, and for each n, we identify the slowest graph where it takes exactly that many steps. Moreover, we establish a stronger bound of roughly [Formula: see text] steps for undirected graphs and an even stronger bound of roughly [Formula: see text] steps for so-called regular graphs. Second, we discuss various complications that one faces when attempting to measure fixation times and colonization times in spatially structured populations, and we propose to measure the real duration of the process, rather than counting the steps of the classic Moran process.
- MeSH
- Population Density MeSH
- Humans MeSH
- Mutation MeSH
- Computer Simulation MeSH
- Population Dynamics * MeSH
- Selection, Genetic MeSH
- Stochastic Processes MeSH
- Computational Biology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
This paper presents a comparative analysis of deterministic and stochastic computational modeling approaches for the optimal control of COVID-19. We formulate a compartmental epidemic model with perturbation by white noise that incorporates various factors influencing disease transmission. By incorporating stochastic effects, the model accounts for uncertainties inherent in real-world epidemic data. We establish the mathematical properties of the model, such as well-posedness and the existence of stationary distributions, which are crucial for understanding long-term epidemic dynamics. Moreover, the study presents an optimal control strategies to mitigate the epidemic's impact, both in deterministic and stochastic sceneries. Reported data from Algeria are used to parameterize the model, ensuring its relevance and applicability to practical satiation. Through numerical simulations, the study provides insights into the effectiveness of different control measures in managing COVID-19 outbreaks. This research contributes to advancing our understanding of epidemic dynamics and informs decision-making processes for epidemic controlling interventions.
- Keywords
- COVID-19 stochastic modeling, Extinction, Simulation, Stationary distribution, Stochastic optimized control,
- MeSH
- COVID-19 * epidemiology prevention & control transmission MeSH
- Humans MeSH
- Computer Simulation MeSH
- SARS-CoV-2 MeSH
- Stochastic Processes MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Comparative Study MeSH
Isogenic bacterial populations can display probabilistic cell-to-cell variation in response to challenges. This phenotypic heterogeneity can affect virulence in animals, but its impact on plant pathogens is unknown. Previously, we showed that expression of the type III secretion system (T3SS) of the plant pathogen Pseudomonas syringae displays phenotypic variation in planta. Here we use flow cytometry and microscopy to investigate single-cell flagellar expression in relation to T3SS expression, showing that both systems undergo phenotypic heterogeneity in vitro in apoplast-mimicking medium and within apoplastic microcolonies throughout colonization of Phaseolus vulgaris. Stochastic, spatial and time factors shape the dynamics of a phenotypically diverse pathogen population that displays division of labour during colonization: effectors produced by T3SS-expressing bacteria act as 'common goods' to suppress immunity, allowing motile flagella-expressing bacteria to increase and leave infected tissue before necrosis. These results showcase the mechanisms of bacterial specialization during plant colonization in an environmentally and agriculturally relevant system.
- MeSH
- Bacterial Proteins metabolism genetics MeSH
- Phaseolus * microbiology MeSH
- Phenotype MeSH
- Flagella * metabolism MeSH
- Plant Diseases * microbiology MeSH
- Flow Cytometry MeSH
- Pseudomonas syringae * pathogenicity genetics metabolism physiology MeSH
- Gene Expression Regulation, Bacterial MeSH
- Type III Secretion Systems * metabolism genetics MeSH
- Virulence MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Bacterial Proteins MeSH
- Type III Secretion Systems * MeSH
Cryoconite holes (water reservoirs) significantly contribute to biodiversity and biogeochemical processes on glacier surfaces. However, the lack of seasonal observations of cryoconite biota limits our knowledge of glacial ecosystem functioning. We studied photoautotrophs, consumers and sediment characteristics (community structure, biomass, elemental composition, organic matter content, δ13C, δ15N) from cryoconite holes in the upper and lower parts of the Forni Glacier ablation zone (Italy) throughout the ablation season. Dominant cyanobacteria were Oscillatoriaceae and Leptolyngbyaceae, while dominant green algae were Zygnemataceae and Chlorellaceae. Tardigrades (Cryobiotus klebelsbergi) were the dominant consumers. The biomass of consumers negatively correlated with the biomass of green algae, indicating that grazing likely controls algal communities in the upper part. Green algae dominated the upper part, while a shift from green algae- to cyanobacteria-dominated communities was observed in the lower part during the season. The increase in δ13C of cryoconite organic matter (OM) in the lower part followed the trend of the community shift of photoautotrophs potentially affected by precipitation. Also, δ13C of tardigrades positively correlated with δ13C of cryoconite OM in the upper part, indicating some cryoconite OM as their food. Some photoautotrophic taxa appeared only on specific dates, and no spatio-temporal changes in the cryoconite general elemental composition were found. Our data indicate that changes in the community structure and biomass of cryoconite biota on the Forni Glacier likely depend on the interplay between phenology, stochastic events (e.g., rainfall) and top-down or bottom-up controls. We demonstrate that multiple observations are essential for understanding the ecology of biota inhabiting cryoconite holes throughout the ablation season.
- Keywords
- Forni glacier, Tardigrada, phenology, stable isotopes, supraglacial habitats, top‐down control,
- Publication type
- Journal Article MeSH
The open nature of Wireless Sensor Networks (WSNs) renders them an easy target to malicious code propagation, posing a significant and persistent threat to their security. Various mathematical models have been studied in recent literature for understanding the dynamics and control of the propagation of malicious codes in WSNs. However, due to the inherent randomness and uncertainty present in WSNs, stochastic modeling approach is essential for a comprehensive understanding of the propagation of malicious codes in WSNs. In this paper, we formulate a general stochastic compartmental model for analyzing the dynamics of malicious code distribution in WSNs and suggest its possible control. We incorporate the stochasticity in the classical deterministic model for the inherent unpredictability in code propagation, which results in a more appropriate representation of the dynamics. A basic theoretical analysis including the stability results of the model with randomness is carried out. Moreover, a higher-order spectral collocation technique is applied for the numerical solution of the proposed stochastic model. The accuracy and numerical stability of the model is presented. Finally, a comprehensive simulation is depicted to verify theoretical results and depict the impact of parameters on the model's dynamic behavior. This study incorporates stochasticity in a deterministic model of malicious codes spread in WSNs with the implementation of spectral numerical scheme which helps to capture these networks' inherent uncertainties and complex nature.
In this study, we present a method for selecting an arbitrary number of distinct configurations from a larger data set by applying k-means clustering to atomistic configuration fingerprints based on the CrystalNN model and radial distribution function (RDF). This approach improves the accuracy of fitting classical molecular dynamics interatomic potentials to density functional theory (DFT) data for both energies and forces while requiring fewer configurations than random selection. We demonstrate this improvement by fitting an embedded-atom method (EAM) potential for titanium, using various configurational sizes from an initial set of 1800 configurations. The k-means clustering consistently achieves better precision and lower standard deviations for a smaller number of configurations than random selection. The results also suggest that only about 30 configurations are sufficient to obtain an EAM model that describes well the full set of 1800 configurations in terms of energies and forces. Additionally, t-distributed stochastic neighbor embedding (t-SNE) method was used to reduce the configuration fingerprints into 2D space, and it revealed an overlap between two configuration subsets with and without Ti vacancy, indicating similar atomic environments. This similarity is captured by k-means clustering but not by random selection. Furthermore, when the overlapping configurations with vacancies were excluded from the k-means algorithm and used only as a test set, their energy and force predictions showed similar precision to those when they were included. This indicates that the overlapping configurations in the 2D t-SNE space indeed imply potential information redundancy among the atomistic configurations.
- Publication type
- Journal Article MeSH
