Reliability engineering of large jit production systems

This paper introduces the rationale and the fundamental elements and algorithms of a reliability engineering methodology, and discusses its application to the design of a large, multi-cell and heterogeneous production system with just-in-time (JIT) deliveries. The failure analysis and the non-reliability costs assessment of such systems is a complex task. In order to cope with such complexity, a two level hierarchical modelling and evaluation framework was developed. According to this framework, the internal behaviour of each manufacturing cell and the overall flow of materials are described, respectively, by local and global models. Local models are firstly obtained from the failure and repair processes of the manufacturing equipment. Then, these models are combined with the failure propagation delays introduced by the work-in-process buffers in order to obtain the system level model. The second part of the paper addresses several design issues of the production system that directly impact the reliability of the deliveries, such as the layout of the plant, the redundancy of the manufacturing equipment and the capacity of the work-in-process buffers. A distinctive feature of the reliability evaluation algorithm resides on the ability to deal with reliability models containing stochastic processes with generalized distributions. This fundamental requirement comes from the fact that repair and failure propagation processes typically present hyper-exponential distributions, e.g., lognormal distributions, that can’t be assessed using the conventional reliability techniques. The paper will also explain how the behavioural and structural characteristics of JIT production systems were explored in order to implement effective evaluation algorithms that fit the requirements of this class of systems.DST -Department of Science and Technology, Government of Kerala(600/09

A DSS for operational management of wastewaters under uncertain conditions

Wastewater treatment facilities of the Ave River basin (located in NW Portugal) are especially vulnerable to infiltration since they present considerable extensions of sewers installed in streams and rivers and collect wastewaters from longstanding sewer networks of five municipalities. The operational management of this complex system involves decision variables such as the selection of the treatment plant where collected wastewater will be treated, with implications for pumped volumes and consequent energy consumption. Aiming to reduce these inflows and increase the management performance of TRATAVE, the company responsible for operating the system, a monitoring network that includes the entire drainage network and treatment facilities operated by the company was designed and implemented. Several flow measurement devices were installed at strategic locations within the sewer network and integrated with a SCADA system responsible for its operation. All measured data was organized in databases. This monitoring platform will support the implementation of a decision support system (DSS) based on a hydrological model of the basin, a hydrodynamic model of the river network and the sewer network. The DSS is being implemented using the Delft-FEWS platform, integrating monitoring data and models. The DSS conceptual framework and the first results of the estimated infiltration volumes are presentedinfo:eu-repo/semantics/publishedVersio

Automated reconstruction and comparison of metabolic models for diverse fungal genomes

info:eu-repo/semantics/publishedVersio

Draft Genome Sequence of Rhodococcus sp. Strain ATCC 49988, a Quinoline-Degrading Bacterium.

We report here the 4.9-Mb genome sequence of a quinoline-degrading bacterium, Rhodococcus sp. strain ATCC 49988. The draft genome data will enable the identification of genes and future genetic modification to enhance traits relevant to heteroaromatic compound degradation

Colon tumor CD31 expression is associated with higher disease-free survival in patients with metabolic syndrome

Metabolic syndrome (MS) is recognized as a risk factor for colon cancer (CC). However, how does the interplay between metabolic dysfunction caused by MS and its individual components affect CC microenvironment and prognosis remains unexplored. Angiogenesis and lymphangiogenesis are fundamental processes for tumor progression and dissemination, ensuring oxygen and nutrient delivery and supporting one of the most important pathways of tumor dissemination, contributing to metastasis. Thus, our aim was to evaluate whether the expression of molecular biomarkers involved in angiogenic and lymphangiogenic processes influenced CC clinicopathological features and prognosis in patients with MS. Clinical and pathological data of 300 patients submitted to CC surgical resection at a single tertiary hospital were retrospectively retrieved from hospital records. Tumor tissue microarrays of archived paraffin-embedded blocks were used to assess CD31, VEGF-A and D2–40 tissue expression by immunohistochemistry. The percentage of stained area was quantified by computerized morphometric analysis. No association between tissue expression of angiogenesis and lymphangiogenesis biomarkers and tumor clinical and pathological characteristics was found. However, in subgroup analysis of patients with MS, dysglycemia was associated with lower D2–40 expression (p = 0.007) and high waist-circumference was associated with higher D2–40 (p = 0.0029) and VEGF-A expression (p = 0.026). In an adjusted Cox proportional hazard model CD31 expression was significantly associated with greater disease-free survival (HR=0.62; 95% CI: 0.41–0.95, p = 0.028). No association was found between D2–40 and VEGF-A expression and CC prognosis. Our data reinforces previous reports that suggest the potential use of CD31 as a CC prognostic biomarker. Additionally, our data further supports the evidence for an interplay between metabolic dysfunction, tumor microenvironment, and vascularization pathways.info:eu-repo/semantics/publishedVersio

Reconciling gene expression data with regulatory network models – a stimulon-based approach for integrated metabolic and regulatory modeling of Bacillus subtilis

The reconstruction of genome-scale metabolic models from genome annotations has become a routine practice in Systems Biology research. The potential of metabolic models for predictive biology is widely accepted by the scientific community, but these same models still lack the capability to account for the effect of gene regulation on metabolic activity. Our focus organism, Bacillus subtilis is most commonly found in soil, being subject to a wide variety of external environmental conditions. This reinforces the importance of the regulatory mechanisms that allow the bacteria to survive and adapt to such conditions. Existing integrated metabolic regulatory models are currently available for only a small number of well-known organisms (e.g E. coli and B. subtilis). The E. coli integrated model was proposed by Covert et al in 2004 and has slowly improved over the years. Goelzer et al. introduced the B. subtilis integrated model in 2008, covering only the central metabolic pathways. Different strategies were used in the two modeling efforts. The E. coli model is defined by a set of Boolean rules (turning genes ON and OFF) accounting mostly for transcription factors, gene interactions, involved metabolites, and some external conditions such as heat shock. The B. subtilis model introduces a set of more complex rules and also incorporates sigma factor activity into the modeling abstraction. Here we propose a genome-scale model for the regulatory network of B. subtilis, using a new stimulon-based approach. A stimulon is defined as the set of genes (that can be a part of the same operon(s) and regulon(s)) that respond in the same set of stimuli. The proposed stimulon-based approach allows for the inclusion of more types of regulation in the model. This methodology also abstracts away much of the complexity of regulatory mechanisms by directly connecting the activity of genes to the presence or absence of associated stimuli, a necessity in the many cases where details of regulatory mechanisms are poorly understood. Our model integrates regulatory network data from the Goelzer et al model, in addition to other available literature data. We then reconciled our model against a large set of high-quality gene expression data (tiled microarrays for 104 different conditions). The stimulons in our model were split or extended to improve consistency with our expression data, and the stimuli in our model were adjusted to improve consistency with the conditions of our expression experiments. The reconciliation with gene expression data revealed a significant number of exact or nearly exact matches between the manually curated regulons/stimulons and pure correlation-based regulons. Our reconciliation analysis of the 2011 SubtiWiki regulon release suggested many gene candidates for regulon extension that were subsequently included in the 2013 SubtiWiki update. Our enhanced model also includes an improved coverage of a wide range of different stress conditions. We then integrated our regulatory model with the latest metabolic reconstruction for B. subtilis, the iBsu1103V2 model (Tanaka et al. 2012). We applied this integrated metabolic regulatory model to the simulation of all growth phenotype data currently available for B. subtilis, demonstrating how the addition of regulatory constraints improved consistency of model predictions with experimentally observed phenotype data. This analysis of growth phenotype data unveiled phenotypes that could only be characterized with the addition of regulatory network constraints. All tools applied in the reconstruction, simulation, and curation of our new regulatory model are now publicly available as a part of the KBase framework. These tools permit the direct simulation of gene expression data using the regulon model alone, as well as the simulation of phenotypes and growth conditions using an integrated metabolic and regulatory model. We will highlight these new tools in the context of our reconstruction and analysis of the B. subtilis regulatory model

Reconciling gene expression data with regulatory network models

The reconstruction of genome-scale metabolic models from genome annotations has become a routine practice in Systems Biology research. The potential of metabolic models for predictive biology is widely accepted by the scientific community, but these same models still lack the capability to account for the effect of gene regulation on metabolic activity. Our focus organism, Bacillus subtilis is most commonly found in soil, being subject to a wide variety of external environmental conditions. This reinforces the importance of the regulatory mechanisms that allow the bacteria to survive and adapt to such conditions. We introduce a manually curated regulatory network for Bacillus subtilis, tapping into the notable resources for B. subtilis regulation. We propose the concept of Atomic Regulon, as a set of genes that share the same ON and OFF gene expression profile across multiple samples of experimental data. Atomic regulon inference uses prior knowledge from curated SEED subsystems, in addition to expression data to infer regulatory interactions. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand/ validate the knowledge of the regulatory networks and gain insights into novel biology

Characteristics of two conditioning regimens cyclophosphamide plus antithymocyte globulin versus cyclophosphamide plus busulfan in allogeneic stem cell transplantation for severe aplastic anemia

Universidade Federal de São Paulo, São Paulo, BrazilHosp Santa Marcelina, São Paulo, BrazilUniversidade Federal de São Paulo, São Paulo, BrazilWeb of Scienc

Growth Characteristics of Kikuyu Grass with Different Sources and Doses of Phosphorus

Growth is defined as the increase in size, volume and mass as a function of time. Growth analysis allows evaluating the final growth of the plant as a whole and the contribution of the different organs in total growth (Benincasa, 1988). The experiment had as objective to evaluate specific leaf area (SLA), leaf area per unit of leaf DM, leaf area ratio (LAR), leaf area per unit of whole plant DM, leaf weight ratio (LWR), leaf weight per unit of plant weight, leaf area index (LAI), leaf area per unit of soil area, leaf/stem ratio (LSR), leaf weight per unit stem weight, of 35 days old kikuyu grass with different sources and doses of P