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.
This paper explores the feasibility of solving a maintenance optimization problem in an interconnected smart grid system, comprising a power grid and a communication network, to reduce system unavailability. The unavailability, which must be in practice under the control of a system operator, is particularly sensitive to critical components in the power grid that must be under preventive maintenance (PM). The main goal is to find an optimal setup of PM within the specified mission time, minimizing system operation costs and reducing time-dependent unavailability. The method for unavailability quantification was remade to include different stochastic models for the unavailability calculation of system components working in different maintenance modes. A cost model is suggested to estimate the cost of various maintenance configurations. By applying these methodological tools designed to benefit users of any complex system, an optimal PM policy was developed for the selected smart grid. This policy reduces grid unavailability by approximately 20% and lowers costs by about 8.5% compared to a configuration without maintenance.
- Keywords
- Power grid, acyclic graph, alternating renewal process, communication network, cost, maintenance optimization, unavailability,
- Publication type
- Journal Article MeSH
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
Diplonemids are among the most abundant and species-rich protists in the oceans. Marine heterotrophic flagellates, including diplonemids, have been suggested to play important roles in global biogeochemical cycles. Diplonemids are also the sister taxon of kinetoplastids, home to trypanosomatid parasites of global health importance, and thus are informative about the evolution of kinetoplastid biology. However, the genomic and cellular complement that underpins diplonemids' highly successful lifestyle is underexplored. At the same time, our framework describing cellular processes may not be as broadly applicable as presumed, as it is largely derived from animal and fungal model organisms, a small subset of extant eukaryotic diversity. In addition to uniquely evolved machinery in animals and fungi, there exist components with sporadic (i.e., "patchy") distributions across other eukaryotes. A most intriguing subset are components ("jötnarlogs") stochastically present in a wide range of eukaryotes but lost in animal and/or fungal models. Such components are considered exotic curiosities but may be relevant to inferences about the complexity of the last eukaryotic common ancestor (LECA) and frameworks of modern cell biology. Here, we use comparative genomics and phylogenetics to comprehensively assess the membrane-trafficking system of diplonemids. They possess several proteins thought of as kinetoplastid specific, as well as an extensive set of patchy proteins, including jötnarlogs. Diplonemids apparently function with endomembrane machinery distinct from existing cell biological models but comparable with other free-living heterotrophic protists, highlighting the importance of including such exotic components when considering different models of ancient eukaryotic genomic complexity and the cell biology of non-opisthokont organisms.
- Keywords
- Euglenozoa, Jotnarlog, endosome, evolutionary cell biology, heterotroph, last eukaryotic common ancestor, membrane trafficking, phylogenetics,
- MeSH
- Biological Evolution MeSH
- Phylogeny MeSH
- Kinetoplastida * physiology genetics MeSH
- 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.
BACKGROUND: Stochastic models are commonly employed in the system and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. Many important models feature complex dynamics, involving a state-space explosion, stiffness, and multimodality, that complicate the quantitative analysis needed to understand their stochastic behavior. Direct numerical analysis of such models is typically not feasible and generating many simulation runs that adequately approximate the model's dynamics may take a prohibitively long time. RESULTS: We propose a new memoization technique that leverages a population-based abstraction and combines previously generated parts of simulations, called segments, to generate new simulations more efficiently while preserving the original system's dynamics and its diversity. Our algorithm adapts online to identify the most important abstract states and thus utilizes the available memory efficiently. CONCLUSION: We demonstrate that in combination with a novel fully automatic and adaptive hybrid simulation scheme, we can speed up the generation of trajectories significantly and correctly predict the transient behavior of complex stochastic systems.
The canonical stop codons of the nuclear genome of the trypanosomatid Blastocrithidia nonstop are recoded. Here, we investigated the effect of this recoding on the mitochondrial genome and gene expression. Trypanosomatids possess a single mitochondrion and protein-coding transcripts of this genome require RNA editing in order to generate open reading frames of many transcripts encoded as 'cryptogenes'. Small RNAs that can number in the hundreds direct editing and produce a mitochondrial transcriptome of unusual complexity. We find B. nonstop to have a typical trypanosomatid mitochondrial genetic code, which presumably requires the mitochondrion to disable utilization of the two nucleus-encoded suppressor tRNAs, which appear to be imported into the organelle. Alterations of the protein factors responsible for mRNA editing were also documented, but they have likely originated from sources other than B. nonstop nuclear genome recoding. The population of guide RNAs directing editing is minimal, yet virtually all genes for the plethora of known editing factors are still present. Most intriguingly, despite lacking complex I cryptogene guide RNAs, these cryptogene transcripts are stochastically edited to high levels.
- MeSH
- Cell Nucleus * genetics metabolism MeSH
- RNA Editing * MeSH
- Genetic Code MeSH
- Genome, Mitochondrial * MeSH
- RNA, Guide, Kinetoplastida genetics metabolism MeSH
- Codon genetics MeSH
- RNA, Messenger genetics metabolism MeSH
- Mitochondria genetics metabolism MeSH
- Open Reading Frames genetics MeSH
- Protozoan Proteins genetics metabolism MeSH
- RNA, Transfer * genetics metabolism MeSH
- Codon, Terminator genetics MeSH
- Trypanosomatina genetics metabolism MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- RNA, Guide, Kinetoplastida MeSH
- Codon MeSH
- RNA, Messenger MeSH
- Protozoan Proteins MeSH
- RNA, Transfer * MeSH
- Codon, Terminator MeSH
The fractional stochastic delay differential equation (FSDDE) is a powerful mathematical tool for modeling complex systems that exhibit both fractional order dynamics and stochasticity with time delays. The purpose of this study is to explore the stability analysis of a system of FSDDEs. Our study emphasizes the interaction between fractional calculus, stochasticity, and time delays in understanding the stability of such systems. Analyzing the moments of the system's solutions, we investigate stochasticity's influence on FSDDS. The article provides practical insight into solving FSDDS efficiently using various numerical techniques. Additionally, this research focuses both on asymptotic as well as Lyapunov stability of FSDDS. The local stability conditions are clearly presented and also the effects of a fractional orders with delay on the stability properties are examine. Through a comprehensive test of a stability criteria, practical examples and numerical simulations we demonstrate the complexity and challenges concern with the analyzing FSDDEs.
- Keywords
- Fractional stochastic delay differential equations, Legendre–Gauss–Lobatto nodes, Spectral method, Stability analysis,
- Publication type
- Journal Article MeSH
The measure of partial mutual information from mixed embedding (PMIME) is an information theory-based measure to accurately identify the direct and directional coupling, termed Granger causality or simply causality, between the observed variables or subsystems of a high-dimensional dynamical and complex system, without any a priori assumptions about the nature of the coupling relationship. In its core, it is a forward selection procedure that aims to iteratively identify the lag-dependence structure of a given observed variable (response) to all the other observed variables (candidate drivers). This model-free approach is capable of detecting nonlinear interactions, abundantly present in real-world complex systems, and it was shown to perform well on multivariate time series of moderately high dimension. However, the PMIME presents some inefficiencies in its performance mainly when applied on strongly stochastic (linear or nonlinear) systems as it may falsely detect non-existent relationships. Moreover, and by construction, the measure cannot extract purely synergetic relationships present in a system. In the current work, the issue of false detections is addressed by introducing an improved resampling significance test and a procedure of rechecking the identified drivers (backward revision). Regarding the inability to detect synergetic relationships, the PMIME is further enhanced by checking pairs as candidate drivers for the response variable after having considered all drivers individually. The effects of these modifications are investigated in a systematic simulation study on properly designed systems involving strong stochasticity, regressor terms with synergetic effects, and a system dimension ranging from 3 to 30. The overall results of the simulations indicate that these modifications indeed improve the performance of PMIME and alleviate to a significant degree the issues of the original algorithm. Guidelines for balancing between accuracy and computational efficiency are also given, particularly relevant for real-world applications. Finally, the measure performance is investigated in the study of futures of various government bonds and stock market indices in the period around COVID-19 pandemic.
- Publication type
- Journal Article MeSH
This study introduces an enhanced self-adaptive wild goose algorithm (SAWGA) for solving economical-environmental-technical optimal power flow (OPF) problems in traditional and modern energy systems. Leveraging adaptive search strategies and robust diversity capabilities, SAWGA distinguishes itself from classical WGA by incorporating four potent optimizers. The algorithm's application to optimize an OPF model on the different IEEE 30-bus and 118-bus electrical networks, featuring conventional thermal power units alongside solar photovoltaic (PV) and wind power (WT) units, addresses the rising uncertainties in operating conditions, particularly with the integration of renewable energy sources (RESs). The inherent complexity of OPF problems in electrical networks, exacerbated by the inclusion of RESs like PV and WT units, poses significant challenges. Traditional optimization algorithms struggle due to the problem's high complexity, susceptibility to local optima, and numerous continuous and discrete decision parameters. The study's simulation results underscore the efficacy of SAWGA in achieving optimal solutions for OPF, notably reducing overall fuel consumption costs in a faster and more efficient convergence. Noteworthy attributes of SAWGA include its remarkable capabilities in optimizing various objective functions, effective management of OPF challenges, and consistent outperformance compared to traditional WGA and other modern algorithms. The method exhibits a robust ability to achieve global or nearly global optimal settings for decision parameters, emphasizing its superiority in total cost reduction and rapid convergence.
