Correct Function Research Articles

MitoMAMMAL: a genome scale model of mammalian mitochondria predicts cardiac and BAT metabolism

Abstract Motivation Mitochondria perform several essential functions in order to maintain cellular homeostasis and mitochondrial metabolism is inherently flexible to allow correct function in a wide range of tissues. Dysregulated mitochondrial metabolism can therefore affect different tissues in different ways which presents experimental challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is therefore useful in gaining in-depth insights into tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism used in research, there is currently no mouse specific mitochondrial metabolic model available. Results In this work, building upon the similarity between human and mouse mitochondrial metabolism, we have created mitoMammal, a genome-scale metabolic model that contains human and mouse specific gene-product reaction rules. MitoMammal is therefore able to model mouse and human mitochondrial metabolism. To demonstrate this feature, using an adapted E-Flux algorithm, we first integrated proteomic data extracted from mitochondria of isolated mouse cardiomyocytes and mouse brown adipocyte tissue. We then integrated transcriptomic data from in vitro differentiated human brown adipose cells and modelled the context specific metabolism using flux balance analysis. In all three simulations, mitoMammal made mostly accurate, and some novel predictions relating to energy metabolism in the context of cardiomyocytes and brown adipocytes. This demonstrates its usefulness in research relating to cardiac disease and diabetes in both mouse and human contexts. Availability and implementation MitoMammal is formatted in SBML3. The code required for constraint-based modelling used in this work is implemented in Python 3 and is available as a Jupyter Notebook. The mitoMammal metabolic model, along with Jupyter notebooks and data used in this work are available at: https://gitlab.com/habermann_lab/mitomammal. Supplementary Information Supplementary data are available at Bioinformatics Advances online.

Development of quantitative PET/MR imaging for measurements of hepatic portal vein input function: a phantom study

BackgroundAccurate pharmacokinetic modelling in PET necessitates measurements of an input function, which ideally is acquired non-invasively from image data. For hepatic pharmacokinetic modelling two input functions need to be considered, to account for the blood supply from the hepatic artery and portal vein. Image-derived measurements at the portal vein are challenging due to its small size and image artifacts caused by respiratory motion. In this work we seek to demonstrate, using phantom experiments, how a dedicated PET/MR protocol can tackle these challenges and potentially provide input function measurements of the portal vein in a clinical setup.MethodsA custom 3D printed PET/MR phantom was constructed to mimic the liver and portal vein. PET/MR acquisitions were made with emulated respiratory motion. The PET/MR imaging protocol consisted of high-resolution anatomical MR imaging of the portal vein, followed by a PET acquisition in parallel to a dedicated motion-tracking MR sequence. Motion tracking and deformation information were extracted from PET data and subsequently used in PET reconstruction to produce dynamic series of motion-free PET images. Anatomical MR images were used post PET reconstruction for partial volume correction of the input function measurements.ResultsReconstruction of dynamic PET data with motion-compensation provided nearly motion-free series of PET frame data, suitable for image derived input function measurements of the portal vein. After partial volume correction, the individual input function measurements were within a 16.1% error range from the true activity in the portal vein compartment at the time of PET acquisition.ConclusionThe proposed protocol demonstrates clinically feasible PET/MR imaging of the liver for pharmacokinetic studies with accurate quantification of the portal vein input function, including correction for respiratory motion and partial volume effects.

Outcomes in hematopoetic cell transplantation in the setting of mold infections in patients with chronic granulomatous disease.

Chronic granulomatous disease (CGD) is a disorder of immunity characterized by phagocyte dysfunction. Mold infections in patients with CGD are often severe and disseminated. We present patient characteristics, microbiological data, and outcomes for 26 patients with CGD who received hematopoietic cell transplantation (HCT) or gene therapy-modified cells (GT) between 2008 and 2019, with proven fungal infection either before or during their transplant. All patients engrafted, and all but one GT recipient had neutrophil recovery and evidence of functional correction. Eighteen patients (69%) are currently alive and 19 patients (73% of total, 90% of patients with repeat imaging performed) had evidence of radiographic improvement. With 3 exceptions, deaths were not principally related to the fungal infection and duration of antecedent infection did not correlate with death. Aspergillus species accounted for the majority of disease (50%), followed by Phellinus species (18%). Osteomyelitis and disseminated disease were common, as only 11 patients (42%) had disease restricted to pneumonia. Triazole therapy was used in all 26 patients, with combination therapy used in 25 (96%). HCT or gene therapy, with appropriate antifungal therapy, are viable therapies for refractory fungal infections in patients with CGD.

MODEL OF MAXIMAL WEIGHTS INVERSE CHAINS FOR THE ANALYSIS OF THE INFLUENCE FACTORS OF THE SOFTWARE COMPLEXES SUPPORT

Context. The problem of identification, formation and restoration of the boundaries of influencing factors, lost as a result of the implementation of multi-layer perceptron models into the models of subjective perception of the object of software complexes support, as well as the applied practical problem of primary monitoring of the frequency manifestation of a given influencing factor in the post-real-time mode, is considered. The object of research is the influencing factors of support of software complexes. Objective – the goal of the work is to develop a model of inverse chains of maximum weights for the analysis of influencing factors of the software complexes support. Method. A model of maximal weights inverse chains for the analysis of the influence factors of the software complexes support was developed for the analysis of the influencing factors of the software complexes support. The developed model provides possibility to identify and form feedback chains of maximum weights for the identification and further analysis of influencing factors that are reflected into the results of the object perception (the supported software complex or its support processes), by the relevant subjects of interaction which directly or indirectly interact with it. Results. Results of the resolved applied practical problem of primary monitoring of the frequency manifestation of a given influencing factor in the post-real-time mode have been provided as an example of the applied practical use of the developed model. The output results of the developed models functioning – are the reverse chains of maximum weights. In the future, the results obtained by the developed model are used to solve the applied-scientific problem of identification, formation and restoration of the boundaries of influencing factors, lost as a result of the implementation of the appropriate models of multilayer perceptron inside the models of subjective perception of the software complexes support. So the developed model of maximal weights inverse chains for the analysis of the influence factors of the software complexes support resolves this applied-scientific problem, initially caused by the implementation of the corresponding multilayer perceptron models inside the model of the subjective perception of the object of software complexessupport. The developed model provides the possibility of carrying out a qualitative analysis of the transformation of the input characteristics of the object of support into the output resulting characteristics of its subjective perception. Conclusions. Developed model allows to resolve the described problems. At the same time, the developed model improves the classical understanding of multilayer perceptron artificial neural networks, as it introduces an additional value to the neurons of hidden layers, which (starting from now) are able to perform a completely new role of influencing factors markers, while in the classical understanding of multilayer perceptron artificial neural networks they did not perform any functions other than arithmetic to ensure the possibility of correct learning and functioning of a multilayer perceptron artificial neural networks.

High-pressure phase transitions of series of catenated nitrogen energetic crystals Nx (x = 4, 8, 10): A comparative DFT-D study.

High-pressure chemistry has advantages in exploring novel energetic materials and is the key to the development of new high-energy materials. The complexity and danger of experimental processes require a deeper understanding by advanced simulation techniques. Therefore, a high-precision comparative DFT-D study was performed to investigate the effect of pressure on series of catenated nitrogen energetic crystals. The results show that there exist phase transitions for N4, N8, and N10 at 4 GPa, 3 GPa, and 2 GPa respectively, which are embodied in various properties of these crystals. Studies on band gap and DOS indicate pressure-induced improvement on the ability for electrons transition from occupied orbitals to empty ones. Hirshfeld surface analysis qualitatively suggests that hydrogen bonding interactions are becoming dominant inter-molecular interactions. The topological analysis quantitatively reveals that pressure is beneficial to enhancing the inter-molecular hydrogen bonding energy, thereby playing an important role in the stability of high-pressure phases. The discussions on mechanical properties imply that pressure can improve the rigidity of these energetic systems and enhance their mechanical properties. Our findings evidence the high-pressure phase transitions for catenated nitrogen energetic crystals, which lay the theoretical foundation for the development of novel energetic materials. Series of catenated nitrogen energetic crystals N4, N8 and N10 were obtained from experiments. Optimizations were performed by GGA/PBE functional and G06 dispersion correction within the framework of CASTEP code, and the cutoff energies of the plane waves were set to 700 eV. The particular moiety in the crystals was extracted by Multiwfn 3.6 and subsequent analysis was conducted by Gaussian 09W package.

The Enhancement of Machine Learning-Based Engine Models Through the Integration of Analytical Functions

The integration of analytical functions into machine learning-based engine models represents a significant advancement in predictive performance and operational efficiency. This paper focuses on the development of hybrid approaches to model engine combustion and temperature indices and on the synergistic effects of combining traditional analytical methods with modern machine learning techniques (such as artificial neural networks) to enhance the accuracy and robustness of such models. The main innovative contribution of this paper is the integration of analytical functions to improve the extrapolation capabilities of the data-driven models. In this work, it is demonstrated that the integrated models achieve superior predictive accuracy and generalization performance across dynamic engine operating conditions, with respect to purely neural network-based models. Furthermore, the analytical corrective functions force the output of the complete model to follow a physical trend and to assume consistent values also outside the domain of values assumed by the input features during the training procedure of the neural networks. This study highlights the potential of this integrative approach based on the implementation of the effects superposition principle. Such an approach also allows us to solve one of the intrinsic issues of data-driven modeling, without increasing the complexity of the training data’s collection and pre-processing.

Costs of Adjustment, Portfolio Separation, and the Dynamic Behavior of Bank Loans and Deposits – Revised

AbstractWe revise the solutions of the dynamic optimization model of Elyasiani, Kopecky, and Van Hoose (1995), and show that the optimal policy function for the case of adjustment costs and no portfolio separation is a vector autoregressive model of order 1, and not of order 2. In addition, the empirical approach of Elyasiani, Kopecky, and Van Hoose (1995) is incorrect. The optimal policy functions for the no portfolio separation case have to be estimated with a panel vector autoregression model and not with a simultaneous equation approach. We also describe how the portfolio separation hypothesis can be tested empirically based on the correct policy functions.

Mechanical Viability and Functionality Assessment of a New Sutureless Endoluminal Microvascular Device: A Preliminary In Vivo Rabbit Study.

Our group has developed a new nitinol endoluminal self-expandable device for microvascular anastomosis. It attaches to each vessel ending with opposite directed microspikes and reaches complete expansion at body temperature, using the nitinol shape memory capacity. The main purpose of this first in vivo trial is to evaluate the mechanical viability of the device and its immediate and early functionality. A recuperation study with seven New Zealand White rabbits was designed. A 1.96 mm outer diameter prototype of the new device was placed on the right femoral artery of each rabbit. Each anastomosis was reassessed on the seventh postoperative day to reevaluate the device function. The average anastomosis time with the new device was 18 min and 45 seg (±0.3 seg). It could be easily placed in all the cases with an average of 1.14 (1) complementary stitches needed to achieve a sealed anastomosis. Patency test was positive for all the cases on the immediate assessment. On the 1 week revision surgery, patency test was negative for the seven rabbits due to blood clot formation inside the device. The new device that we have developed is simple to use and shows correct immediate functionality. On the early assessment, the presence of a foreign body in the endoluminal space caused blood clot formation. We speculate that a heparin eluting version of the device could avoid thrombosis formation. We consider that the results obtained can be valuable for other endoluminal sutureless devices.

An Investigation of Internal Drag Correction Methodology for Wind Tunnel Test Data in a High-Speed Air-Breathing Vehicle Using Numerical Analysis

AbstractAccurate understanding of aerodynamic characteristics is critical for air vehicle design. This study applies the impulse-momentum theorem to estimate internal drag of an asymmetric air vehicle. Both computational fluid dynamics (CFD) and wind tunnel tests were employed. Numerical analysis using STAR-CCM + was conducted across various Mach numbers, while wind tunnel tests were conducted under only specific Mach number conditions. The measured data from wind tunnel tests, which is considered representative, were compared with CFD plane-averaged values, indicating fairly good agreement. However, discrepancies arose when comparing CFD cell-averaged values with the plane-averaged values. To address this, a correction function was developed to describe the differences. It was observed that the ratio of internal drag coefficient from the cell-averaged values to that of the plane-averaged values followed an exponential function of Mach number. This approach allows wind tunnel data to be transformed into a form akin to cell-averaged values, enhancing internal drag estimation accuracy. Future research will explore additional methodologies to further refine and validate the proposed approach.

Disorders of puberty and neurodevelopment: A shared etiology?

The neuroendocrine control of puberty and reproduction is fascinatingly complex, with up- and down-regulation of key reproductive hormones during fetal, infantile, and later childhood periods that determine the correct function of the hypothalamic-pituitary-gonadal axis and the timing of puberty. Neuronal development is a vital element of these processes, and multiple conditions of disordered puberty and reproduction have their etiology in abnormal neuronal migration or function. Although there are numerous documented cases across multiple conditions wherein patients have both neurodevelopmental disorders and pubertal abnormalities, this has mostly been described ad hoc and the associations are not clearly documented. In this review, we aim to describe the overlap between these two groups of conditions and to increase awareness to ensure that puberty and reproductive function are carefully monitored in patients with neurodevelopmental conditions, and vice versa. Moreover, this commonality can be explored for clues about the disease mechanisms in these patient groups and provide new avenues for therapeutic interventions for affected individuals.

Model-driven development for functional correctness of avionics systems: a verification framework for SysML specifications

AbstractCurrently, the most widespread software quality assurance methods in the avionics domain are semi-automated reviews and testing. However, their effort grows disproportionately to the size of the system under development. Also, these methods cannot achieve exhaustive coverage due to the complexity of today’s avionics systems and their potentially infinite set of combinations of possible inputs and system states. Furthermore, the later software issues are detected in the development process, the more expensive it is to fix them. To overcome these issues, a model-driven verification approach for modeling and analyzing avionics systems in early phases of the development is presented. To this end, semantics is given to SysML v2 models by a mapping to a theorem prover encoding. The development of a dedicated SysML v2 profile supporting event-driven data flow specifications, the encoding of corresponding structures in the theorem prover Isabelle, and a generator creating theorems from SysML v2 models are presented. The approach is evaluated by formally proving a representative liveness property of a hierarchical system model from the avionics domain. Since liveness properties can be negated only by infinite data sequences and thus cannot be covered exhaustively by testing, this case study demonstrates the added value for meeting typical safety requirements in the avionics domain. The results can be transferred from avionics to other domains, as well.

Constitutive Modeling of Anisotropic Plasticity with ML Algorithms in Metals

Nuclear reactors hold significant promise for mitigating the environmental crisis and achieving a carbon-free world. The advancement of nuclear technology, however, is intricately tied to the materials used in reactors, with metals and alloys forming their core components. Understanding the plasticity and degradation of these materials is crucial to prevent critical reactor failures caused by factors such as radiation-induced embrittlement, creep, and fuel-cladding interactions. While traditional plasticity modeling has relied on constitutive laws and yield functions, the advent of machine learning (ML) and artificial intelligence has opened new avenues for this field. This paper explores the potential of data-driven approaches to enhance plasticity modeling for metals used in nuclear reactors. Recent studies have leveraged ML algorithms, including support vector machines and artificial neural networks, to model yield surfaces and correct theoretical yield functions with experimental data. Despite their accuracy, these models often lack interpretability and generality. To address these challenges, we investigate the applicability of various ML algorithms, i.e. neural networks, logistic regressions, decision trees, K-nearest neighbors, and Gaussian processes (GPs), in developing data-oriented flow rules for plasticity modeling. The paper demonstrates the superiority of GPs in terms of interpretability and generality. Using stress data generated from PyLabFEA, we compared the performance of these algorithms, highlighting the intrinsic uncertainties associated with GP predictions. Our findings indicate that Gaussian process classifiers (GPCs) offer a promising approach for modeling plasticity in metals, providing a balance between precision and physical insight. Additionally, this work presents a deep neural network (DNN) to model anisotropic Hill-type plasticity with perfect test data accuracy (accuracy score: 1.0), which is often hard to achieve with a classification problem. This shows the superiority of DNNs in terms of accuracy in modeling yield functions. The implications of our results for bridging the scale gaps in macrolevel simulations are discussed, and future directions for incorporating microstructural features into ML-based plasticity models are proposed. Overall, the ML framework presented in this paper can be employed for modeling constitutive relations that can be incorporated within traditional Finite Element Method (FEM)–based codes. Specifically, the GPC or DNN developed in this work can be readily swapped with the yield function within the so-called return mapping algorithm of nonlinear FEM-based plasticity subroutines to indicate yielding. Consequently, this merging of ML and FEM will allow us to leverage the geometric representational ability of FEM alongside the superior modeling capabilities of the ML algorithms.

Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability

In the field of Cooperative Intelligent Transport Systems (C-ITSs), the traditional approach to testing often emphasizes technological parameters, leaving the validation of message content insufficiently addressed. Since the content of these messages is crucial for the correct functioning of C-ITS, this article demonstrates the potential for automated evaluation of C-ITS message content against relevant standards. It leverages our novel tools, Karlos and C-ITS SIM, to facilitate this process. Through detailed laboratory testing and data analysis, the study showcases the effectiveness of these automated solutions in enhancing the accuracy and reliability of message content validation.

Iron deficiency and its impact on post-vaccination immunity: a review

Modern vaccination programs are vital to overcoming the burden of infectious diseases and saving countless lives. The effective functioning of the adaptive immune system involves the interaction of many factors. Recent clinical studies have shown a significant role of iron in developing an immune response to infection and vaccination. Lymphocytes, the immune system's leading cells, cannot fully perform their functions without access to the circulating iron. The amount of iron bound to blood transferrin depends on its intake from food, and it is reduced during active inflammation due to the increase in the production of hepcidin, the main hormone regulating iron metabolism. Since iron deficiency conditions and chronic inflammatory processes are common, the potential impact of iron deficiency on the immune response warrants extensive study. The review presents data confirming the importance of iron for the correct functioning of the immune system and information on the effect of iron deficiency on the development of post-vaccination immunity.

Impact of bolt positioning on the stiffness of angular support brackets

Bolts and screws are usually preferred over other joining techniques when assembly and disassembly operations are required. However, they add extra weight to the system, so it is essential to reduce their size and weight by optimizing bolt parameters. Additionally, the stiffness of bolted members is very crucial; those with low stiffness may affect the correct functioning of the other connected components. This study focuses on the impact produced by bolt positions, specifically their center of gravity and spacing among them, on the stiffness of angular L-shaped brackets using Finite Element Analysis. Bending and torsional loads were applied to the free end of the member and deformations were recorded under these loads. The results revealed that a triangular bolt pattern is recommended for three bolts, whereas for four or more bolts, the pattern should be uniform to avoid stress concentrations. Moreover, the center of gravity and bolt spacing have a significant impact on stiffness. By optimizing bolt spacing and the center of gravity, a combination of six bolts can be replaced with four bolts of the same size while maintaining the same stiffness.

Inactivity of Stat3 in sensory and non-sensory cells of the mature cochlea.

Signal transducer and activator of transcription 3 (Stat3) plays a role in various cellular processes such as differentiation, inflammation, cell survival and microtubule dynamics, depending on the cell type and the activated signaling pathway. Stat3 is highly expressed in the hair cells and supporting cells of the cochlea and is essential for the differentiation of mouse hair cells in the early embryonic stage. However, it is unclear how Stat3 contributes to the correct function of cells in the organ of Corti postnatally. To investigate this, an inducible Cre/loxp system was used to knock out Stat3 in either the outer hair cells or the supporting cells. The results showed that the absence of Stat3 in either the outer hair cells or the supporting cells resulted in hearing loss without altering the morphology of the organ of Corti. Molecular analysis of the outer hair cells revealed an inflammatory process with increased cytokine production and upregulation of the NF-kB pathway. However, the absence of Stat3 in the supporting cells resulted in reduced microtubule stability. In conclusion, Stat3 is a critical protein for the sensory epithelium of the cochlea and hearing and functions in a cell and function-specific manner.

Enhancement Method Based on Multi-Strategy Improved Pelican Optimization Algorithm and Application to Low-Illumination Forest Canopy Images

Enhancement is a crucial step in the field of image processing, as it significantly improves image analysis and understanding. One of the most commonly used methods for image contrast enhancement is the incomplete beta function (IBF). However, the key challenge lies in determining the optimal parameters for the IBF. This paper introduces a multi-strategy improved pelican optimization algorithm (MIPOA) to address the low-illumination color image enhancement problem. The MIPOA algorithm utilizes a nonlinear decreasing coefficient to boost the exploration ability and convergence speed, whereas the Hardy–Weinberg principle compensates for the unsound exploitation mechanism. Additionally, the diversity variation operation improves the ability of the algorithm to escape local optimal solutions. The performance of the proposed MIPOA algorithm was evaluated using a benchmark function and was found to outperform five variant algorithms in extensive comparisons. To further harness the potential of the MIPOA algorithm, the authors propose a low-light forest canopy image enhancement method based on the MIPOA algorithm. The MIPOA algorithm searches for the optimal parameters of the IBF, leading to fast contrast enhancement of the image. The segmented gamma correction function is designed to enhance the brightness of the low-light forest canopy images. In determining the optimal parameters of IBF, the MIPOA algorithm demonstrates superior performance compared to other intelligent algorithms in the feature similarity index (FSIM), entropy, and contrast improvement index (CII) of 75%, 58.33%, and 75%, respectively. The proposed MIPOA-based enhancement method achieves a moderate pixel mean and surpasses the conventional enhancement method with an average gradient of 91.67%. The experimental results indicate that the MIPOA effectively addresses the limitations of low optimization accuracy in IBF parameters, and the enhancement method based on the MIPOA provides a more efficacious approach for enhancing low-light forest canopy images.

A Comparative Study of Metaheuristic Feature Selection Algorithms for Respiratory Disease Classification.

The correct diagnosis and early treatment of respiratory diseases can significantly improve the health status of patients, reduce healthcare expenses, and enhance quality of life. Therefore, there has been extensive interest in developing automatic respiratory disease detection systems. Most recent methods for detecting respiratory disease use machine and deep learning algorithms. The success of these machine learning methods depends heavily on the selection of proper features to be used in the classifier. Although metaheuristic-based feature selection methods have been successful in addressing difficulties presented by high-dimensional medical data in various biomedical classification tasks, there is not much research on the utilization of metaheuristic methods in respiratory disease classification. This paper aims to conduct a detailed and comparative analysis of six widely used metaheuristic optimization methods using eight different transfer functions in respiratory disease classification. For this purpose, two different classification cases were examined: binary and multi-class. The findings demonstrate that metaheuristic algorithms using correct transfer functions could effectively reduce data dimensionality while enhancing classification accuracy.

Rotaphone-D, A New Model of Six-Degree-of-Freedom Seismic Sensor: Description and Performance

Abstract A new model of the Rotaphone-D short-period seismic sensor with six degrees of freedom is introduced. The basis of the instrument is horizontal and vertical geophones in a special paired arrangement. The instrument is designed for simultaneous and collocated measurements of rotational and translational components of ground motion. The basic principle of measurement is briefly described. Careful calibration is necessary for this type of measurement, which has two parts: a preliminary calibration based on laboratory measurements of the characteristics of the individual geophones, and a subsequent in situ calibration, which takes into account the actual physical conditions during field measurements and is performed during the processing of the measured data. The effect of the calibration is demonstrated by specific laboratory tests. The laboratory tests have confirmed the correct functioning of the instrument. The frequency range of Rotaphone-D is from 2 to 80 Hz and is therefore optimal for monitoring local seismicity. The instrument was subsequently used in field measurements during two several-month measurement campaigns in California, United States. Examples of six-component records from two sites—The Geysers and Long Valley Caldera—are presented and briefly interpreted from a seismological perspective. Basic instrument parameters are given. The instrument is lightweight, portable, easy to install, and offers several interesting applications, which are briefly discussed. Six-component measurements (translational and rotational) have become a powerful tool, especially in volcanic seismology and in monitoring induced seismicity.

Multris: Functional Verification of Multiparty Message Passing in Separation Logic

We introduce Multris, a separation logic for verifying functional correctness of programs that combine multiparty message-passing communication with shared-memory concurrency. The foundation of our work is a novel concept of multiparty protocol consistency, which guarantees safe communication among a set of parties, provided each party adheres to its prescribed protocol. Our concept of protocol consistency is inspired by the bottom-up approach for multiparty session types. However, by considering it in the context of separation logic instead of a type system, we go further in terms of generality by supporting new notions of implicit transfer of knowledge and implicit transfer of resources. We develop tactics for automatically verifying protocol consistency and for reasoning about message-passing operations in Multris. We evaluate Multris on a range of examples, including the well-known two- and three-buyer protocols, as well as a new verification benchmark based on Chang and Roberts's ring leader election protocol. To ensure the reliability of our work, we prove soundness of Multris w.r.t. a low-level channel semantics using the Iris framework in Coq.