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ESREL2021, 31st European Safety and Reliability Conference, Angers, FRANCE, 19-/09/2021 - 23/09/2021; The quality and the ability of the road infrastructures play a very important role to ensure safe and convenient transportation. Current human needs oblige us to optimize the use of our travel resources. The autonomous driving is proposed as a solution to make the transport more efficient. The autonomous vehicle contains cameras and lidars that can communicate with the road infrastructure to understand the environment where they are traveling. Thus, the existing road infrastructure must therefore be developed and maintained to address the availability needs induced by the new uses of mobility. In consequence, the maintenance of the road infrastructure must be optimized. In previous work, a maintenance policy for lines and road cracks pavement of the road infrastructure was proposed by considering it as a 4 components system: median strip line, emergency line, broken center line and pavement. The proposed strategy is based on the individual optimal maintenance plan of each component. Then, over a finite planning horizon, the scheduled maintenance actions are grouped together to ensure both the proper functioning of the system and to minimize the cost of maintenance. To improve this previous work, a new long-term horizon (30 years) dynamic grouping maintenance strategy is proposed, dynamically considering new monitoring data. This new algorithm is applied to the maintenance optimization of the road infrastructure, using the Long-Term Pavement Performance database and French National Road 4 feedback data.

This paper focuses on new efficient and adaptive optimization algorithms to cope with the maintenance grouping problem for series, parallel, and complex systems. We propose a Particle Swarm Optimization approach to cope with small and medium problem sizes, and that will be used to benchmark existing heuristic solutions such as Genetic Algorithms. To address scalability and adaptability issues, we propose a new dynamic optimization algorithm based on a clustering technique. This clustering-based solution is formulated using an Integer Linear Programing approach to guarantee the convergence to global optimal solutions of the considered problem. We show the performance of the proposed approaches with a clear advantage to the clustering-based algorithm that we recommend for large industrial systems.

International audience; Maintenance actions constitute critical tasks that ensure the availability of industrial systems and improve their operating safety. However, maintenance faces numerous challenges and is no longer limited to guarantying availability. It has become a strategic concern and abides by imposing quality, safety, and cost requirements. Finding optimal grouping strategies of maintenance activities is an NP-hard problem that is well studied in the literature, and for which various economic models and optimization approaches are proposed. While most models found in literature use heuristics, such as evolutionary algorithms, to locate cost-reducing grouping strategies, context-specific constraints that could arise within each system are not taken into consideration. For large complex systems, heuristic approaches cannot guarantee the convergence to a feasible solution. Therefore, we propose a new scalable and adaptive optimization algorithm based on a clustering approach to group maintenance activities in multi-component complex systems. The proposed Constrained Clustering-based approach takes into consideration domain-dependant constraints and provides grouping strategies in negligible times.

Clostridium difficile is a significant concern as a nosocomial pathogen, and genetic tools are important when analyzing the physiology of such organisms so that the underlying physiology/pathogenesis of the organisms can be studied. Here, we used TargeTron to investigate the role of selenoproteins in C. difficile Stickland metabolism and found that a TargeTron insertion into selD, encoding the selenophosphate synthetase that is essential for the specific incorporation of selenium into selenoproteins, results in a significant growth defect and a global loss of selenium incorporation. However, because of potential polar effects of the TargeTron insertion, we developed a CRISPR-Cas9 mutagenesis system for C. difficile. This system rapidly and efficiently introduces site-specific mutations into the C. difficile genome (20–50% mutation frequency). The selD CRISPR deletion mutant had a growth defect in protein-rich medium and mimicked the phenotype of a generated TargeTron selD mutation. Our findings suggest that Stickland metabolism could be a target for future antibiotic therapies and that the CRISPR-Cas9 system can introduce rapid and efficient modifications into the C. difficile genome.

Three years after its first 2010 enactment, the French government proposed a timeline for the development of a new metro network providing new rapid transit lines in the Ile de France region. Implemented by the Société du Grand Paris (SGP), the Grand Paris Express (GPE) thus became the largest transport project in Europe. As in any new railway project, a safety record must be established by the SGP. Among all criteria that had to be investigated, the ability of the network manager to prevent and detect broken rails was a particularly sensitive point. Indeed, beyond the obvious consequences induced by a broken rail impacting the safety of passengers, such an event has a very strong impact on the availability of the infrastructure, which is a key point for all metro line automation projects. The SGP therefore required some decision support tools to enable us to evaluate the consequences of a broken rail on the network operating conditions and, moreover, to determine the best monitoring strategy and the adequacy of the maintenance policy to prevent broken rails. Based on a former study commissioned by RATP (the historic operator and infrastructure manager of the Paris metro network) dealing with the evaluation and optimization of detection and prevention of broken rails in a metro line automation context, the SGP wanted to possess an equivalent decision support tool customised to the needs and the characteristics of the GPE. Nevertheless, in the GPE context, the network that was considered does not yet exist. Moreover, no equivalent system exists in the world. No feedback databases are therefore available to estimate reliability parameters necessary for such decision support tools. Therefore, the present paper proposes an original approach combining physical modelling of rail deterioration with statistics to overcome this limitation.

Safety is a core issue in the railway operation. In particular, as witnessed by accident/incident statistics, railway level crossing (LX) safety is one of the most critical points in railways. In the present paper, a Bayesian network (BN) based framework for causal reasoning related to risk analysis is proposed. It consists of a set of integrated stages, namely risk scenario definition, real field data collection and processing, BN model establishment and model performance validation. In particular, causal structural constraints are introduced to the framework for the purpose of combining empirical knowledge with automatic learning approaches, thus to identify effective causalities and avoid inappropriate structural connections. Then, the proposed framework is applied to risk analysis of LX accidents in France. In details, the BN risk model is established on the basis of real field data and the model performance is validated. Moreover, forward and reverse inferences based on the BN risk model are performed to predict LX accident occurrence and quantify the contribution degree of various impacting factors respectively, so as to identify the riskiest factors. Besides, influence strength and sensitivity analyses are further carried out to scrutinize the influence strength of various causal factors on the LX accident occurrence likelihood and determine which factors the LX accident occurrence is most sensitive to. The main outputs of our study attest that the proposed framework is sound and effective in terms of risk reasoning analysis and offers significant insights on exploring practical recommendations to prevent LX accidents.

ESREL 2020 - PSAM 15 - 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference, VENISE, ITALIE, 01-/11/2020 - 05/11/2020; The quality of the road infrastructure plays a major role in the road safety, especially for the autonomous vehicles (AV). The AV contains cameras and lidars able to detect the road markings, obstacles and the other vehicles. The road markings help the AV to identify the path runway and to understand their localization. Thus, the AV must interact with the road infrastructure to drive around without any human interactions with a high automatization level. The failures of the components of the road infrastructure (pavement and road markings) are incompatible phenomena with the operation of AV. To ensure a good evolution of this kind of vehicles, the infrastructure and the vehicles must coordinate, each providing a certain level of service. Thus, an efficient road maintenance must be considered. The proposed paper suggests a maintenance policy for the road infrastructure by grouping the maintenance strategies of the road markings and the pavement. This strategy considers the road infrastructure as serial system. A genetic algorithm is used to group the maintenance activities. This methodology is applied to feedback datasets from both the French National Road 4 and the American pavement using the Long-Term Pavement Performance (LTPP) database.

D-Ala-D-Ala ligase, encoded by ddl genes, is responsible for the synthesis of a dipeptide, D-Ala-D-Ala, an essential precursor of bacterial peptidoglycan. In Clostridioides difficile, the single ddl gene is located upstream of the ddlR gene, which encodes a putative transcriptional regulator. Using mutational and transcriptional analysis and DNA-binding assays, DdlR was found to be a direct activator of the ddl ddlR operon. DdlR is a member of the MocR/GabR-type proteins that have aminotransferase-like, pyridoxal 5'-phosphate-binding domains. A DdlR mutation that prevented covalent binding of pyridoxal 5'-phosphate abolished the ability of DdlR to activate transcription. Addition of D-Ala-D-Ala to the medium inactivated DdlR, reducing dipeptide biosynthesis. In contrast, D-Ala-D-Ala limitation caused a dramatic increase in expression from the ddl promoter. Though uncommon for transcription regulators, C. difficile DdlR is essential, as the ddlR null mutant cells could not grow even in complex laboratory media in the absence of D-Ala-D-Ala. A dyad symmetry sequence, which is located immediately upstream of the -35 region of the ddl promoter, serves as an important element of the DdlR-binding site. This sequence is conserved upstream of putative DdlR targets in other bacteria of classes Clostridia and Bacilli, indicating a similar mode of regulation of these genes.

International audience; For the human pathogen Clostridioides (also known as Clostridium) difficile, the ability to adapt to nutrient availability is critical for its proliferation and production of toxins during infection. Synthesis of the toxins is regulated by the availability of certain carbon sources, fermentation products and amino acids (e.g. proline, cysteine, isoleucine, leucine and valine). The effect of proline is attributable at least in part to its role as an inducer and substrate of D-proline reductase (PR), a Stickland reaction that regenerates NAD+ from NADH. Many Clostridium spp. use Stickland metabolism (co-fermentation of pairs of amino acids) to generate ATP and NAD+ . Synthesis of PR is activated by PrdR, a proline-responsive regulatory protein. Here we report that PrdR, in the presence of proline, represses other NAD+ -generating pathways, such as the glycine reductase and succinate-acetyl CoA utilization pathways leading to butyrate production, but does so indirectly by affecting the activity of Rex, a global redox-sensing regulator that responds to the NAD+ /NADH ratio. Our results indicate that PR activity is the favored mechanism for NAD+ regeneration and that both Rex and PrdR influence toxin production. Using the hamster model of C. difficile infection, we revealed the importance of PrdR-regulated Stickland metabolism in the virulence of C. difficile.

Clostridium difficile must form a spore to survive outside the gastrointestinal tract. The factors that trigger sporulation in C. difficile remain poorly understood. Previous studies have suggested that a link exists between nutritional status and sporulation initiation in C. difficile . In this study, we investigated the impact of the global nutritional regulator CodY on sporulation in C. difficile strains from the historical 012 ribotype and the current epidemic 027 ribotype. Sporulation frequencies were increased in both backgrounds, demonstrating that CodY represses sporulation in C. difficile . The 027 codY mutant exhibited a greater increase in spore formation than the 012 codY mutant. To determine the role of CodY in the observed sporulation phenotypes, we examined several factors that are known to influence sporulation in C. difficile . Using transcriptional reporter fusions and quantitative reverse transcription-PCR (qRT-PCR) analysis, we found that two loci associated with the initiation of sporulation, opp and sinR , are regulated by CodY. The data demonstrate that CodY is a repressor of sporulation in C. difficile and that the impact of CodY on sporulation and expression of specific genes is significantly influenced by the strain background. These results suggest that the variability of CodY-dependent regulation is an important contributor to virulence and sporulation in current epidemic isolates. This report provides further evidence that nutritional state, virulence, and sporulation are linked in C. difficile . IMPORTANCE This study sought to examine the relationship between nutrition and sporulation in C. difficile by examining the global nutritional regulator CodY. CodY is a known virulence and nutritional regulator of C. difficile , but its role in sporulation was unknown. Here, we demonstrate that CodY is a negative regulator of sporulation in two different ribotypes of C. difficile . We also demonstrate that CodY regulates known effectors of sporulation, Opp and SinR. These results support the idea that nutrient limitation is a trigger for sporulation in C. difficile and that the response to nutrient limitation is coordinated by CodY. Additionally, we demonstrate that CodY has an altered role in sporulation regulation for some strains.

Toxin synthesis and endospore formation are two of the most critical factors that determine the outcome of infection by Clostridioides difficile. The two major toxins, TcdA and TcdB, are the principal factors causing damage to the host. Spores are the infectious form of C. difficile, permit survival of the bacterium during antibiotic treatment and are the predominant cell form that leads to recurrent infection. Toxin production and sporulation have their own specific mechanisms of regulation, but they share negative regulation by the global regulatory protein CodY. Determining the extent of such regulation and its detailed mechanism is important for understanding the linkage between two apparently independent biological phenomena and raises the possibility of creating new ways of limiting infection. The work described here shows that a codY null mutant of a hypervirulent (ribotype 027) strain is even more virulent than its parent in a mouse model of infection and that the mutant expresses most sporulation genes prematurely during exponential growth phase. Moreover, examining the expression patterns of mutants producing CodY proteins with different levels of residual activity revealed that expression of the toxin genes is dependent on total CodY inactivation, whereas most sporulation genes are turned on when CodY activity is only partially diminished. These results suggest that, in wild-type cells undergoing nutrient limitation, sporulation genes can be turned on before the toxin genes.

ESREL 2019 - 29th European Safety and Reliability Conference, Hanovre, ALLEMAGNE, 22-/09/2019 - 26/09/2019; The quality and reliability of road infrastructure and its equipment play a major role in road safety. This is especially true if we are interested in autonomous vehicle traffic. Markings line are detected by camera. This option needs an accurate maintenance strategy. In that way, their retroreflection luminance is measured for evaluating their degradation. A line is replaced according to the yearly inspection. The consensus strategy is to define a degradation models of markings based on a regression method. Such models provide a prediction of failure occurrences and a preventive replacement is engaged before this instant. This paper proposes alternatively a systematic age-based replacement by strategic areas. In previous study, an Agglomerative Hierarchical Clustering identifies markings with the same degradation model. The feedback database is strongly censored because the exact failure time isn't observed. To deal with this drawback, an WEM Algoritm was proposed to determine a Weibull model. For each strategic area, a cost function can therefore be defined according to both maintenance costs and the associated Weibull model. The optimum replacement period was finally defined as the minimum point of the cost function. The French National Road 4 (NR4) brocken centerline illustrates this approach. Segmented into five clusters, each one admits its own optimum replacement step. The proposed replacement strategy is generic its application is not limited to our case study.

ESREL 2019, 29th European Safety and Reliability Conference,; Road markings channel traffic flows and guide road users visually, especially at night. Their quality and reliability are, therefore, important issues which must be closely followed to ensure the safety of road users and especially their perceptibility for the future autonomous cars. Thus, previous study has shown that it is possible to segment markings of the center line into maintenance strategic areas. However, this study didn't take into account all the characteristics of road infrastructures that could have an impact on the degradation of markings. Indeed, depending on the structure and complexity of all road infrastructures, these road markings could present a different degradation process over the time and in specific areas. So, the identification of these areas can help road managers to develop localized maintenance strategies. In this study, a Factor Analysis of Mixed Data (FAMD) combined with an Agglomerative Hierarchical Clustering (AHC) is proposed as the method to identify these areas or clusters in the case of the French National Road 4 (NR4).

ESREL 2019, 29th European Safety and Reliability Conference, Hanovre, ALLEMAGNE, 22-/09/2019 - 26/09/2019; Each industrial field seems affected by revolutionary innovations that will push forward the quality and variety of disparate services: driverless trains running 24 hours/day, self-driving cars and interconnected objects are only some of the latest trends. The intrinsic common aspect among all these promising concepts is the increase in reliability needs: human intervention for restoring and rescuing is not tolerated anymore. Despite this consideration, electronic equipment datasheets of many vendors do measure reliability through fuzzy metrics, the MTBF (Mean Time Between Failures) being the first among those. Firstly, this paper aims to provide information on the origin of the MTBF and on the reason why it is nowadays the most common mean for referring to reliability. Furthermore, the reasons behind the fallacy of the MTBF are explained. Using MTBF as synthetic indicator of reliability does often represents the wrong choice. Indeed, high MTBF figures could be measured even if the actual reliability is low. However, MTBF use is often imposed by regulations or contracts, and some examples are reported in this paper related to the railway signaling domain. Finally, alternative structured ways of communicating about reliability, based on further concepts than the bare MTBF, are provided as conclusion of the article.

The symptoms of Clostridium difficile infection (CDI) are attributed largely to two C. difficile toxins, TcdA and TcdB. Significant efforts have been devoted to developing vaccines targeting both toxins through parenteral immunization routes. However, C. difficile is an enteric pathogen, and mucosal/oral immunization would be particularly useful to protect the host against CDI, considering that the gut is the main site of disease onset and progression. Moreover, vaccines directed only against toxins do not target the cells and spores that transmit the disease. Previously, we constructed a chimeric vaccine candidate, mTcd138, comprised of the glucosyltransferase and cysteine proteinase domains of TcdB and the receptor binding domain of TcdA. In this study, to develop an oral vaccine that can target both C. difficile toxins and colonization/adhesion factors, we expressed mTcd138 in a nontoxigenic C. difficile (NTCD) strain, resulting in strain NTCD_mTcd138. Oral immunization with spores of NTCD_mTcd138 provided mice full protection against infection with a hypervirulent C. difficile strain, UK6 (ribotype 027). The protective strength and efficacy of NTCD_mTcd138 were further evaluated in the acute CDI hamster model. Oral immunization with spores of NTCD_mTcd138 also provided hamsters significant protection against infection with 2 × 10(4) UK6 spores, a dose 200-fold higher than the lethal dose of UK6 in hamsters. These results imply that the genetically modified, nontoxigenic C. difficile strain expressing mTcd138 may represent a novel mucosal vaccine candidate against CDI.

The quality and reliability of road infrastructure and its equipment play a major role in road safety. This is especially true for autonomous car traffic guided mainly by a GPS system that is, unfortunately, neither precise nor reliable. In order to improve the guidance systems, one option could be to equip the vehicle with a camera reading road markings. Such solution requires maintenance strategies guaranteeing markings' perceptibility to the human eye or the autonomous car camera. Currently, the retroreflection luminance of markings is measured for evaluating marking degradation. An important remaining step is a life time analysis depending on the inspection strategy. Since the exact failure time isn't generally observed, feedback database contain many censured data: the left-censure corresponding to a marking failing before the first inspection, the interval censure that corresponds to markings failing between two inspections, and the right-censure corresponding to a marking that never fails. In the literature, a Weibull analysis was proposed to estimate the markings reliable distributions using the Maximum Likelihood through the Newton-Raphson method. Facing with censored data, this approach couldn't be computed without introducing strong bias in the reliability estimation. For generic interval-censored data, Pradhan and Kundu proposed an alternative, based on the EM algorithm. In our study an extension of the EM algorithm processing left and right censures is proposed. Finally, this algorithm is applicable for all kind of observations, whatever the censure nature. After introducing this EM extension, the paper focuses on the fact that computations are simpler than the Newton-Raphson methods and censored-data are directly estimated. The French National Road 4 markings case is considered to illustrate the proposed approach. Moreover, the proposed algorithm being generic, its application is, of course, not limited to our road marking case study.

The increasing incidence and severity of infection by Clostridium difficile have stimulated attempts to develop new antimicrobial therapies. We report here the relative abilities of two antibiotics (metronidazole and vancomycin) in current use for treating C. difficile infection and of a third antimicrobial, surotomycin, to kill C. difficile cells at various stages of development and to inhibit the production of the toxin proteins that are the major virulence factors. The results indicate that none of the drugs affects the viability of spores at 8× MIC or 80× MIC and that all of the drugs kill exponential-phase cells when provided at 8× MIC. In contrast, none of the drugs killed stationary-phase cells or inhibited toxin production when provided at 8× MIC and neither vancomycin nor metronidazole killed stationary-phase cells when provided at 80× MIC. Surotomycin, on the other hand, did kill stationary-phase cells when provided at 80× MIC but did so without inducing lysis.

International audience; Synthesis of the major toxin proteins of the diarrheal pathogen, Clostridium difficile, is dependent on the activity of TcdR, an initiation (sigma) factor of RNA polymerase. The synthesis of TcdR and the activation of toxin gene expression are responsive to multiple components in the bacterium's nutritional environment, such as the presence of certain sugars, amino acids, and fatty acids. This review summarizes current knowledge about the mechanisms responsible for repression of toxin synthesis when glucose or branched-chain amino acids or proline are in excess and the pathways that lead to synthesis of butyrate, an activator of toxin synthesis. The regulatory proteins implicated in these mechanisms also play key roles in modulating bacterial metabolic pathways, suggesting that C. difficile pathogenesis is intimately connected to the bacterium's metabolic state.

International audience; System degradation modelling is a key problem when performing any type of reliability study. It is used to determine the quality of the computed reliability indicators and prognostic estimates. However, the mathematical models that are commonly used in reliability studies (Markov chains, gamma process. etc.) make certain assumptions that can lead to a loss of information regarding the degradation dynamics. Many studies have shown how Dynamic Bayesian Networks (DBNs) can be relevant in representing complex multicomponent systems and in performing reliability studies. In a previous paper [10], Donat et al. introduced a type of degradation model based on DBNs called a graphical duration model (GDM) for discrete-state systems to represent a wide range of duration models. This paper introduces a new type of degradation model based on the GDM approach that integrates the concept of conditional sojourn time distributions (CSTDs) to improve the degradation modelling. It introduces the possibility of considering many degradation dynamics simultaneously. It allows the degradation modelling to be adapted based on newly available observations of a system to account for changes in dynamics over time. A comparative study of the presented methodology and the GDM approach was conducted using simulated data to demonstrate the advantages of this new approach in performing prognostic computations. Only two coexisting dynamics are considered in the experiments for the sake of simplicity.

Clostridium difficile -associated disease (CDAD) constitutes a large majority of nosocomial diarrhea cases in industrialized nations and is mediated by the effects of two secreted toxins, toxin A (TcdA) and toxin B (TcdB). Patients who develop strong antitoxin antibody responses can clear C. difficile infection and remain disease free. Key toxin-neutralizing epitopes have been found within the carboxy-terminal receptor binding domains (RBDs) of TcdA and TcdB, which has generated interest in developing the RBD as a viable vaccine target. While numerous platforms have been studied, very little data describes the potential of DNA vaccination against CDAD. Therefore, we created highly optimized plasmids encoding the RBDs from TcdA and TcdB in which any putative N -linked glycosylation sites were altered. Mice and nonhuman primates were immunized intramuscularly, followed by in vivo electroporation, and in these animal models, vaccination induced significant levels of both anti-RBD antibodies (blood and stool) and RBD-specific antibody-secreting cells. Further characterization revealed that sera from immunized mice and nonhuman primates could detect RBD protein from transfected cells, as well as neutralize purified toxins in an in vitro cytotoxicity assay. Mice that were immunized with plasmids or given nonhuman-primate sera were protected from a lethal challenge with purified TcdA and/or TcdB. Moreover, immunized mice were significantly protected when challenged with C. difficile spores from homologous (VPI 10463) and heterologous, epidemic (UK1) strains. These data demonstrate the robust immunogenicity and efficacy of a TcdA/B RBD-based DNA vaccine in preclinical models of acute toxin-associated and intragastric, spore-induced colonic disease.

Clostridium difficile is a nosocomial pathogen that causes a serious toxin-mediated enteric disease in humans. Reducing C. difficile toxin production could significantly minimize its pathogenicity and improve disease outcomes in humans. This study investigated the efficacy of two, food-grade, plant-derived compounds, namely trans-cinnamaldehyde (TC) and carvacrol (CR) in reducing C. difficile toxin production and cytotoxicity in vitro. Three hypervirulent C. difficile isolates were grown with or without the sub-inhibitory concentrations of TC or CR, and the culture supernatant and the bacterial pellet were collected for total toxin quantitation, Vero cell cytotoxicity assay and RT-qPCR analysis of toxin-encoding genes. The effect of CR and TC on a codY mutant and wild type C. difficile was also investigated. Carvacrol and TC substantially reduced C. difficile toxin production and cytotoxicity on Vero cells. The plant compounds also significantly down-regulated toxin production genes. Carvacrol and TC did not inhibit toxin production in the codY mutant of C. difficile, suggesting a potential codY-mediated anti-toxigenic mechanism of the plant compounds. The antitoxigenic concentrations of CR and TC did not inhibit the growth of beneficial gut bacteria. Our results suggest that CR and TC could potentially be used to control C. difficile, and warrant future studies in vivo.

Reliability analysis has become an integral part of system design and operation. This is especially true for systems performing critical tasks, such as mass transportation systems. This explains the numerous advances in the field of reliability modeling. More recently, some studies involving the use of Bayesian networks have been proven relevant to represent complex systems and perform reliability studies. In previous works, a generic methodology was introduced for developing a decision support tool to evaluate complex systems maintenance strategies. This article deals with development of such a decision tool dedicated to the maintenance of Paris metro rails. Indeed, owing to fulfillment of high-performance levels of safety and availability (the latter being especially critical at peak hours), operators need to estimate, hour by hour their ability to prevent or to detect broken rails. To address this problem, a decision support tool was developed, the aim of this article is to evaluate, compare and optimize various operating and maintenance strategies.

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented. © 2017. This is the authors' manuscript to the article. The final authenticated version is available online at: http://journals.sagepub.com/doi/10.1177/1748006X17712661

The pathogenicity of Clostridium difficile is linked to its ability to produce two toxins: TcdA and TcdB. The level of toxin synthesis is influenced by environmental signals, such as phosphotransferase system (PTS) sugars, biotin, and amino acids, especially cysteine. To understand the molecular mechanisms of cysteine-dependent repression of toxin production, we reconstructed the sulfur metabolism pathways of C. difficile strain 630 in silico and validated some of them by testing C. difficile growth in the presence of various sulfur sources. High levels of sulfide and pyruvate were produced in the presence of 10 mM cysteine, indicating that cysteine is actively catabolized by cysteine desulfhydrases. Using a transcriptomic approach, we analyzed cysteine-dependent control of gene expression and showed that cysteine modulates the expression of genes involved in cysteine metabolism, amino acid biosynthesis, fermentation, energy metabolism, iron acquisition, and the stress response. Additionally, a sigma factor (SigL) and global regulators (CcpA, CodY, and Fur) were tested to elucidate their roles in the cysteine-dependent regulation of toxin production. Among these regulators, only sigL inactivation resulted in the derepression of toxin gene expression in the presence of cysteine. Interestingly, the sigL mutant produced less pyruvate and H 2 S than the wild-type strain. Unlike cysteine, the addition of 10 mM pyruvate to the medium for a short time during the growth of the wild-type and sigL mutant strains reduced expression of the toxin genes, indicating that cysteine-dependent repression of toxin production is mainly due to the accumulation of cysteine by-products during growth. Finally, we showed that the effect of pyruvate on toxin gene expression is mediated at least in part by the two-component system CD2602-CD2601.

Clostridium difficile, a proteolytic Gram-positive anaerobe, has emerged as a significant nosocomial pathogen. Stickland fermentation reactions are thought to be important for growth of C. difficile and appear to influence toxin production. In Stickland reactions, pairs of amino acids donate and accept electrons, generating ATP and reducing power in the process. Reduction of the electron acceptors proline and glycine requires the d-proline reductase (PR) and the glycine reductase (GR) enzyme complexes, respectively. Addition of proline in the medium increases the level of PR protein but decreases the level of GR. We report the identification of PrdR, a protein that activates transcription of the PR-encoding genes in the presence of proline and negatively regulates the GR-encoding genes. The results suggest that PrdR is a central metabolism regulator that controls preferential utilization of proline and glycine to produce energy via the Stickland reactions.

Clostridium perfringens is an anaerobic Gram-positive pathogen that causes many human and animal diseases, including food poisoning and gas gangrene. C. perfringens lacks flagella but possesses type IV pili (TFP). We have previously shown that C. perfringens can glide across an agar surface in long filaments composed of individual bacteria attached end to end and that two TFP-associated proteins, PilT and PilC, are needed for this. To discover additional gene products that play a role in gliding, we developed a plasmid-based mariner transposon mutagenesis system that works effectively in C. perfringens. More than 10,000 clones were screened for mutants that lacked the ability to move away from the edge of a colony. Twenty-four mutants (0.24%) were identified that fit the criteria. The genes containing insertions that affected gliding motility fell into nine different categories. One gene, CPE0278, which encodes a homolog of the SagA cell wall-dependent endopeptidase, acquired distinct transposon insertions in two independent mutants. sagA mutants were unable to form filaments due to a complete lack of end-to-end connections essential for gliding motility. Complementation of the sagA mutants with a wild-type copy of the gene restored gliding motility. We constructed an in-frame deletion mutation in the sagA gene and found that this mutant had a phenotype similar to those of the transposon mutants. We hypothesize that the sagA mutant strains are unable to form the molecular complexes which are needed to keep the cells in an end-to-end orientation, leading to separation of daughter cells and the inability to carry out gliding motility.

Fidaxomicin (FDX) is a novel antimicrobial agent with narrow-spectrum and potent bactericidal activity against Clostridium difficile. In recent clinical trials, FDX was superior to vancomycin in preventing recurrences of C. difficile infection. A possible mechanism of reducing recurrence may be through an inhibitory effect on sporulation. The effect of FDX and its major metabolite, OP-1118, on C. difficile growth and sporulation kinetics was compared with that of vancomycin, metronidazole, and rifaximin. Drugs at subminimum inhibitory concentrations (sub-MICs) were added to cells at an early stationary phase of growth; this was followed by collection of cells at various intervals for quantitation of total viable cell and heat-resistant spore counts on taurocholate-containing media. The effect of the drugs at 2–2.5× MIC on the expression of sporulation genes in C. difficile was also compared using quantitative reverse-transcriptase polymerase chain reaction. Both FDX and OP-1118 (1/4× MIC) inhibited sporulation when added to early-stationary-phase cells in C. difficile strains, including the epidemic NAP1/BI/027 strain. In contrast, vancomycin, metronidazole, and rifaximin (at similar sub-MICs) did not inhibit sporulation. The number of spores following treatment with comparator drugs increased to the same level as the no-drug control treatment. Expression of mother cell–specific (spoIIID) and forespore-specific (spoIIR) sporulation genes also was inhibited by FDX and OP-1118 but not significantly by vancomycin. Both FDX and OP-1118 (unlike vancomycin, rifaximin, and metronidazole) effectively inhibited sporulation by C. difficile. The inhibitory effect of FDX on C. difficile sporulation may contribute to its superior performance in sustaining clinical response and reducing recurrences and may also be beneficial in decreasing shedding and transmission of this pathogen.

Reliability analysis is an integral part of system design and operating. Moreover, it can be an input to optimize maintenance policies. Recently, Bayesian Networks (BN) and Dynamic Bayesian Networks (DBN) have been proved relevant to represent complex systems and perform reliability studies. The major drawback of this approach comes from the constraint on the state sojourn times which are necessarily exponentially distributed, as in usual markovian approaches. Therefore, a new formalism was introduced to avoid this constraint: the Graphical Duration Models (GDM). This paper aims to quantify the reliability estimation error due to an exponential approximation when the system follows other kinds of sojourn time's distributions. Finally results obtained by DBN and GDM will be compared.

This article describes a hybrid diagnosis system based on the combined use of sensor data (local information) and structural knowledge (global information). The approach is illustrated on an application that involves the detection of broken rail for railway infrastructure. Recently, there have been a large number of attempts to solve diagnosis problems by mixing low-level and high-level data. The inherent difficulty of combining different levels of data is offset by the benefits that accrue from additional knowledge: prior information can improve the understanding of sensor data. To introduce the application context, the paper describes first the importance of rail diagnosis. The technological solutions for broken rail detection are then listed. Since the used sensor is able to detect other kind of rail defects or singularities, the defect classification phase also involves the problem of distinguishing between real (broken rail) and false defects (rail singularities). With the help of prior information extracted from an infrastructure database, a Bayesian network is designed to infer the probabilities of membership of real or false defect classes on the basis of previous decisions. The performance of the combined use of these probabilities and sensor processing are finally presented. They demonstrate the advantage of the approach based on both a local description of the defect and a description of its environment.

SAFEPROCESS'15 - 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, PARIS, France, 02-/09/2015 - 04/09/2015; In any reliability analysis, the degradation modeling is a key point. Indeed, the accuracy of all reliability indicators and prognosis estimations will directly depends on the quality of the degradation modeling. Commonly used stochastic models such as Markov chains, Gamma process... are generally based on some strong assumptions on the stochastic properties of the considered degradation process that can induce some prejudicial losses of information. In many studies the Dynamic Bayesian Networks formalism (DBN) has been proved relevant to perform reliability studies, since a modeling based on discrete and finite states space is acceptable. In this paper some specific DBN structures will be introduced in order to improve the degradation modeling and perform reliability analysis, integrating the concept of conditional sojourn time distributions that allow considering simultaneously several degradation dynamics. A comparative study with simulated data has been finally carried.

Fidaxomicin (FDX) is a novel antimicrobial agent with narrow-spectrum and potent bactericidal activity against Clostridium difficile. In recent clinical trials, FDX was superior to vancomycin in preventing recurrences of C. difficile infection. A possible mechanism of reducing recurrence may be through an inhibitory effect on sporulation. The effect of FDX and its major metabolite, OP-1118, on C. difficile growth and sporulation kinetics was compared with that of vancomycin, metronidazole, and rifaximin. Drugs at subminimum inhibitory concentrations (sub-MICs) were added to cells at an early stationary phase of growth; this was followed by collection of cells at various intervals for quantitation of total viable cell and heat-resistant spore counts on taurocholate-containing media. The effect of the drugs at 2–2.5× MIC on the expression of sporulation genes in C. difficile was also compared using quantitative reverse-transcriptase polymerase chain reaction. Both FDX and OP-1118 (1/4× MIC) inhibited sporulation when added to early-stationary-phase cells in C. difficile strains, including the epidemic NAP1/BI/027 strain. In contrast, vancomycin, metronidazole, and rifaximin (at similar sub-MICs) did not inhibit sporulation. The number of spores following treatment with comparator drugs increased to the same level as the no-drug control treatment. Expression of mother cell–specifi c( spoIIID) and forespore-specifi c( spoIIR) sporulation genes also was inhibited by FDX and OP-1118 but not significantly by vancomycin. Both FDX and OP-1118 (unlike vancomycin, rifaximin, and metronidazole) effectively inhibited sporulation by C. difficile. The inhibitory effect of FDX on C. difficile sporulation may contribute to its superior performance in sustaining clinical response and reducing recurrences and may also be beneficial in decreasing shedding and transmission of this pathogen.

Clostridium difficile is a Gram-positive, spore forming, anaerobic, intestinal bacterium and is the most common cause of antibiotic-associated colitis. For many years this organism was considered genetically intractable, but in the past 10 years, multiple methods have been developed or adapted for genetic manipulation of C. difficile. This unit describes the molecular techniques used for genetic modification of this organism, including methods for gene disruption, complementation, plasmid introduction and integration, and cross-species conjugations.