943 resultados para Regulatory model
Resumo:
With the recent regulatory reforms in a number of countries, railways resources are no longer managed by a single party but are distributed among different stakeholders. To facilitate the operation of train services, a train service provider (SP) has to negotiate with the infrastructure provider (IP) for a train schedule and the associated track access charge. This paper models the SP and IP as software agents and the negotiation as a prioritized fuzzy constraint satisfaction (PFCS) problem. Computer simulations have been conducted to demonstrate the effects on the train schedule when the SP has different optimization criteria. The results show that by assigning different priorities on the fuzzy constraints, agents can represent SPs with different operational objectives.
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Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.
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We introduce a genetic programming (GP) approach for evolving genetic networks that demonstrate desired dynamics when simulated as a discrete stochastic process. Our representation of genetic networks is based on a biochemical reaction model including key elements such as transcription, translation and post-translational modifications. The stochastic, reaction-based GP system is similar but not identical with algorithmic chemistries. We evolved genetic networks with noisy oscillatory dynamics. The results show the practicality of evolving particular dynamics in gene regulatory networks when modelled with intrinsic noise.
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In this paper we construct a mathematical model for the genetic regulatory network of the lactose operon. This mathematical model contains transcription and translation of the lactose permease (LacY) and a reporter gene GFP. The probability of transcription of LacY is determined by 14 binding states out of all 50 possible binding states of the lactose operon based on the quasi-steady-state assumption for the binding reactions, while we calculate the probability of transcription for the reporter gene GFP based on 5 binding states out of 19 possible binding states because the binding site O2 is missing for this reporter gene. We have tested different mechanisms for the transport of thio-methylgalactoside (TMG) and the effect of different Hill coefficients on the simulated LacY expression levels. Using this mathematical model we have realized one of the experimental results with different LacY concentrations, which are induced by different concentrations of TMG.
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Recent studies have shown that small genetic regulatory networks (GRNs) can be evolved in silico displaying certain dynamics in the underlying mathematical model. It is expected that evolutionary approaches can help to gain a better understanding of biological design principles and assist in the engineering of genetic networks. To take the stochastic nature of GRNs into account, our evolutionary approach models GRNs as biochemical reaction networks based on simple enzyme kinetics and simulates them by using Gillespie’s stochastic simulation algorithm (SSA). We have already demonstrated the relevance of considering intrinsic stochasticity by evolving GRNs that show oscillatory dynamics in the SSA but not in the ODE regime. Here, we present and discuss first results in the evolution of GRNs performing as stochastic switches.
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Currently, well established clinical therapeutic approaches for bone reconstruction are restricted to the transplantation of autografts and allografts, and the implantation of metal devices or ceramic-based implants to assist bone regeneration. Bone grafts possess osteoconductive and osteoinductive properties, their application, however, is associated with disadvantages. These include limited access and availability, donor site morbidity and haemorrhage, increased risk of infection, and insufficient transplant integration. As a result, recent research focuses on the development of complementary therapeutic concepts. The field of tissue engineering has emerged as an important alternative approach to bone regeneration. Tissue engineering unites aspects of cellular biology, biomechanical engineering, biomaterial sciences and trauma and orthopaedic surgery. To obtain approval by regulatory bodies for these novel therapeutic concepts the level of therapeutic benefit must be demonstrated rigorously in well characterized, clinically relevant animal models. Therefore, in this PhD project, a reproducible and clinically relevant, ovine, critically sized, high load bearing, tibial defect model was established and characterized as a prerequisite to assess the regenerative potential of a novel treatment concept in vivo involving a medical grade polycaprolactone and tricalciumphosphate based composite scaffold and recombinant human bone morphogenetic proteins.
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Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.
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During the last several decades, the quality of natural resources and their services have been exposed to significant degradation from increased urban populations combined with the sprawl of settlements, development of transportation networks and industrial activities (Dorsey, 2003; Pauleit et al., 2005). As a result of this environmental degradation, a sustainable framework for urban development is required to provide the resilience of natural resources and ecosystems. Sustainable urban development refers to the management of cities with adequate infrastructure to support the needs of its population for the present and future generations as well as maintain the sustainability of its ecosystems (UNEP/IETC, 2002; Yigitcanlar, 2010). One of the important strategic approaches for planning sustainable cities is „ecological planning‟. Ecological planning is a multi-dimensional concept that aims to preserve biodiversity richness and ecosystem productivity through the sustainable management of natural resources (Barnes et al., 2005). As stated by Baldwin (1985, p.4), ecological planning is the initiation and operation of activities to direct and control the acquisition, transformation, disruption and disposal of resources in a manner capable of sustaining human activities with a minimum disruption of ecosystem processes. Therefore, ecological planning is a powerful method for creating sustainable urban ecosystems. In order to explore the city as an ecosystem and investigate the interaction between the urban ecosystem and human activities, a holistic urban ecosystem sustainability assessment approach is required. Urban ecosystem sustainability assessment serves as a tool that helps policy and decision-makers in improving their actions towards sustainable urban development. There are several methods used in urban ecosystem sustainability assessment among which sustainability indicators and composite indices are the most commonly used tools for assessing the progress towards sustainable land use and urban management. Currently, a variety of composite indices are available to measure the sustainability at the local, national and international levels. However, the main conclusion drawn from the literature review is that they are too broad to be applied to assess local and micro level sustainability and no benchmark value for most of the indicators exists due to limited data availability and non-comparable data across countries. Mayer (2008, p. 280) advocates that by stating "as different as the indices may seem, many of them incorporate the same underlying data because of the small number of available sustainability datasets". Mori and Christodoulou (2011) also argue that this relative evaluation and comparison brings along biased assessments, as data only exists for some entities, which also means excluding many nations from evaluation and comparison. Thus, there is a need for developing an accurate and comprehensive micro-level urban ecosystem sustainability assessment method. In order to develop such a model, it is practical to adopt an approach that uses a method to utilise indicators for collecting data, designate certain threshold values or ranges, perform a comparative sustainability assessment via indices at the micro-level, and aggregate these assessment findings to the local level. Hereby, through this approach and model, it is possible to produce sufficient and reliable data to enable comparison at the local level, and provide useful results to inform the local planning, conservation and development decision-making process to secure sustainable ecosystems and urban futures. To advance research in this area, this study investigated the environmental impacts of an existing urban context by using a composite index with an aim to identify the interaction between urban ecosystems and human activities in the context of environmental sustainability. In this respect, this study developed a new comprehensive urban ecosystem sustainability assessment tool entitled the „Micro-level Urban-ecosystem Sustainability IndeX‟ (MUSIX). The MUSIX model is an indicator-based indexing model that investigates the factors affecting urban sustainability in a local context. The model outputs provide local and micro-level sustainability reporting guidance to help policy-making concerning environmental issues. A multi-method research approach, which is based on both quantitative analysis and qualitative analysis, was employed in the construction of the MUSIX model. First, a qualitative research was conducted through an interpretive and critical literature review in developing a theoretical framework and indicator selection. Afterwards, a quantitative research was conducted through statistical and spatial analyses in data collection, processing and model application. The MUSIX model was tested in four pilot study sites selected from the Gold Coast City, Queensland, Australia. The model results detected the sustainability performance of current urban settings referring to six main issues of urban development: (1) hydrology, (2) ecology, (3) pollution, (4) location, (5) design, and; (6) efficiency. For each category, a set of core indicators was assigned which are intended to: (1) benchmark the current situation, strengths and weaknesses, (2) evaluate the efficiency of implemented plans, and; (3) measure the progress towards sustainable development. While the indicator set of the model provided specific information about the environmental impacts in the area at the parcel scale, the composite index score provided general information about the sustainability of the area at the neighbourhood scale. Finally, in light of the model findings, integrated ecological planning strategies were developed to guide the preparation and assessment of development and local area plans in conjunction with the Gold Coast Planning Scheme, which establishes regulatory provisions to achieve ecological sustainability through the formulation of place codes, development codes, constraint codes and other assessment criteria that provide guidance for best practice development solutions. These relevant strategies can be summarised as follows: • Establishing hydrological conservation through sustainable stormwater management in order to preserve the Earth’s water cycle and aquatic ecosystems; • Providing ecological conservation through sustainable ecosystem management in order to protect biological diversity and maintain the integrity of natural ecosystems; • Improving environmental quality through developing pollution prevention regulations and policies in order to promote high quality water resources, clean air and enhanced ecosystem health; • Creating sustainable mobility and accessibility through designing better local services and walkable neighbourhoods in order to promote safe environments and healthy communities; • Sustainable design of urban environment through climate responsive design in order to increase the efficient use of solar energy to provide thermal comfort, and; • Use of renewable resources through creating efficient communities in order to provide long-term management of natural resources for the sustainability of future generations.
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Increasing globalisation and local expansion of Higher Education presents challenges to manage quality of the education services. This study investigated key stakeholders' perspectives on what constitutes key elements and attributes of an effective Quality Assurance (QA) system in Higher Education. The findings highlighted the need for: i) legislation to support a strong QA regulatory framework, ii) independence of the QA agency, iii) development of minimum quality standards through broad stakeholder involvement, and iv) a cyclical approach. The findings of this study proposed a QA model which has implications for strengthening of HE QA systems of Small State.
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The legal arrangements for the management of water resources are currently a complex matrix of rules of various kinds. These rules perform a diverse range of functions. Some are part of what may be described as the macro-legal system for the governance of water resources. This includes paralegal rules in the form of statements of value, objective, outcome or principles . Others are part of the micro-legal system for the governance of water resources. This includes traditional legal rules in the form of statements of standards in relation to individual conduct, behaviour or decision making. These legal arrangements may be international, regional, national or local. Accordingly some apply to nation states within the international community. Others apply to the regulatory agencies making decisions about water resources within nation states. Ultimately most of these legal arrangements apply to those who use and develop water resources for particular purposes and in particular locations. In accordance with this framework, rules explain how water resources should be used in particular circumstances and how decisions should be made to ensure the effective planning and regulation of water resources.
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Epithelial-mesenchymal transition (EMT) is a feature of migratory cellular processes in all stages of life, including embryonic development and wound healing. Importantly, EMT features cluster with disease states such as chronic fibrosis and cancer. The dissolution of the E-cadherin-mediated adherens junction (AJ) is a key preliminary step in EMT and may occur early or late in the growing epithelial tumour. This is a first step for tumour cells towards stromal invasion, intravasation, extravasation and distant metastasis. The AJ may be inactivated in EMT by directed E-cadherin cleavage; however, it is increasingly evident that the majority of AJ changes are transcriptional and mediated by an expanding group of transcription factors acting directly or indirectly to repress E-cadherin expression. A review of the current literature has revealed that these factors may regulate each other in a hierarchical pattern where Snail1 (formerly Snail) and Snail2 (formerly Slug) are initially induced, leading to the activation of Zeb family members, TCF3, TCF4, Twist, Goosecoid and FOXC2. Within this general pathway, many inter-regulatory relationships have been defined which may be important in maintaining the EMT phenotype. This may be important given the short half-life of Snail1 protein. We have investigated these inter-regulatory relationships in the mesenchymal breast carcinoma cell line PMC42 (also known as PMC42ET) and its epithelial derivative, PMC42LA. This review also discusses several newly described regulators of E-cadherin repressors including oestrogen receptor-α and new discoveries in hypoxia- and growth factor-induced EMT. Finally, we evaluated how these findings may influence approaches to current cancer treatment.
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Objectives Self-regulation refers to the practice of using self-imposed restrictions to protect oneself from situations that are, or are perceived to be, unsafe. Within the driving context, self-regulation refers the compensatory practices that some older adults adopt to restrict their driving to situations in which they feel safe. However, the way in which demographic, functional, and psychosocial factors, and the interactions between these factors, influence older adults’ driving self-regulation is not well understood. Improving this understanding could lead to new ways of considering the mobility concerns faced by older drivers. Method A systematic review of the current literature was conducted to explore this issue. Twenty-nine empirical studies investigating the factors associated with older adults’ self-regulatory driving behaviors were examined. Results The review findings were used to construct the Multilevel Older Persons Transportation and Road Safety (MOTRS) model. The MOTRS model proposes that individual and environmental factors such as age, gender, and the availability of alternative transportation predict older adults’ practice of driving-related self-regulation. However, these variables influence self-regulation through psychosocial variables such as driving confidence, affective attitude, and instrumental attitude toward driving. Discussions The MOTRS model extends previous attempts to model older adults’ driving by focusing on a novel target, driving self-regulation, and by including a wider range of predictors identified on the basis of the systematic literature review. This focus enables consideration of broader mobility issues and may inform new strategies to support the mobility of older adults.
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The anthocyanin biosynthetic pathway is regulated by a transcription factor complex consisting of an R2R3 MYB, a bHLH, and a WD40. Although R2R3 MYBs belonging to the anthocyanin-activating class have been identified in many plants, and their role well elucidated, the subgroups of bHLH implicated in anthocyanin regulation seem to be more complex. It is not clear whether these potential bHLH partners are biologically interchangeable with redundant functions, or even if heterodimers are involved. In this study, AcMYB110, an R2R3 MYB isolated from kiwifruit (Actinidia sp.) showing a strong activation of the anthocyanin pathway in tobacco (Nicotiana tabacum) was used to examine the function of interacting endogenous bHLH partners. Constitutive expression of AcMYB110 in tobacco leaves revealed different roles for two bHLHs, NtAN1 and NtJAF13. A hierarchical mechanism is shown to control the regulation of transcription factors and consequently of the anthocyanin biosynthetic pathway. Here, a model is proposed for the regulation of the anthocyanin pathway in Solanaceous plants in which AN1 is directly involved in the activation of the biosynthetic genes, whereas JAF13 is involved in the regulation of AN1 transcription.
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This paper documents the longitudinal and reciprocal relations among behavioral sleep problems, emotional and attentional self-regulation in a population sample of 4109 children participating in the Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC) – Infant Cohort. Maternal reports of children’s sleep problems and self-regulation were collected at five time points from infancy to 8-9 years of age. Longitudinal structural equation modeling supported a developmental cascade model in which sleep problems have a persistent negative effect on emotional regulation, which in turn contributes to ongoing sleep problems and poorer attentional regulation in children over time. Findings suggest that sleep behaviors are a key target for interventions that aim to improve children’s self-regulatory capacities.
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Introduction: Ankylosing spondylitis (AS) is unique in its pathology where inflammation commences at the entheses before progressing to an osteoproliferative phenotype generating excessive bone formation that can result in joint fusion. The underlying mechanisms of this progression are poorly understood. Recent work has suggested that changes in Wnt signalling, a key bone regulatory pathway, may contribute to joint ankylosis in AS. Using the proteoglycan-induced spondylitis (PGISp) mouse model which displays spondylitis and eventual joint fusion following an initial inflammatory stimulus, we have characterised the structural and molecular changes that underlie disease progression. Methods: PGISp mice were characterised 12 weeks after initiation of inflammation using histology, immunohistochemistry (IHC) and expression profiling. Results: Inflammation initiated at the periphery of the intervertebral discs progressing to disc destruction followed by massively excessive cartilage and bone matrix formation, as demonstrated by toluidine blue staining and IHC for collagen type I and osteocalcin, leading to syndesmophyte formation. Expression levels of DKK1 and SOST, Wnt signalling inhibitors highly expressed in joints, were reduced by 49% and 63% respectively in the spine PGISp compared with control mice (P < 0.05) with SOST inhibition confirmed by IHC. Microarray profiling showed genes involved in inflammation and immune-regulation were altered. Further, a number of genes specifically involved in bone regulation including other members of the Wnt pathway were also dysregulated. Conclusions: This study implicates the Wnt pathway as a likely mediator of the mechanism by which inflammation induces bony ankylosis in spondyloarthritis, raising the potential that therapies targeting this pathway may be effective in preventing this process.