Anxiety and depression in patients with gastrointestinal cancer: does knowledge of cancer diagnosis matter?

<p>Abstract</p> <p>Background</p> <p>Gastrointestinal cancer is the first leading cause of cancer related deaths in men and the second among women in Iran. An investigation was carried out to examine anxiety and depression in this group of patients and to investigate whether the knowledge of cancer diagnosis affect their psychological distress.</p> <p>Methods</p> <p>This was a cross sectional study of anxiety and depression in patients with gastrointestinal cancer attending to the Tehran Cancer Institute. Anxiety and depression was measured using the Hospital Anxiety and Depression Scale (HADS). This is a widely used valid questionnaire to measure psychological distress in cancer patients. Demographic and clinical data also were collected to examine anxiety and depression in sub-group of patients especially in those who knew their cancer diagnosis and those who did not.</p> <p>Results</p> <p>In all 142 patients were studied. The mean age of patients was 54.1 (SD = 14.8), 56% were male, 52% did not know their cancer diagnosis, and their diagnosis was related to esophagus (29%), stomach (30%), small intestine (3%), colon (22%) and rectum (16%). The mean anxiety score was 7.6 (SD = 4.5) and for the depression this was 8.4 (SD = 3.8). Overall 47.2% and 57% of patients scored high on both anxiety and depression. There were no significant differences between gender, educational level, marital status, cancer site and anxiety and depression scores whereas those who knew their diagnosis showed a significant higher degree of psychological distress [mean (SD) anxiety score: knew diagnosis 9.1 (4.2) vs. 6.3 (4.4) did not know diagnosis, P < 0.001; mean (SD) depression score: knew diagnosis 9.1 (4.1) vs. 7.9 (3.6) did not know diagnosis, P = 0.05]. Performing logistic regression analysis while controlling for demographic and clinical variables studied the results indicated that those who knew their cancer diagnosis showed a significant higher risk of anxiety [OR: 2.7, 95% CI: 1.1–6.8] and depression [OR: 2.8, 95% CI: 1.1–7.2].</p> <p>Conclusion</p> <p>Psychological distress was higher in those who knew their cancer diagnosis. It seems that the cultural issues and the way we provide information for cancer patients play important role in their improved or decreased psychological well-being.</p

Locomotor Adaptation versus Perceptual Adaptation when Stepping Over an Obstacle with a Height Illusion

Background During locomotion, vision is used to perceive environmental obstacles that could potentially threaten stability; locomotor action is then modified to avoid these obstacles. Various factors such as lighting and texture can make these environmental obstacles appear larger or smaller than their actual size. It is unclear if gait is adapted based on the actual or perceived height of these environmental obstacles. The purposes of this study were to determine if visually guided action is scaled to visual perception, and to determine if task experience influenced how action is scaled to perception. Methodology/Principal Findings Participants judged the height of two obstacles before and after stepping over each of them 50 times. An illusion made obstacle one appear larger than obstacle two, even though they were identical in size. The influence of task experience was examined by comparing the perception-action relationship during the first five obstacle crossings (1–5) with the last five obstacle crossings (46–50). In the first set of trials, obstacle one was perceived to be 2.0 cm larger than obstacle two and subjects stepped 2.7 cm higher over obstacle one. After walking over the obstacle 50 times, the toe elevation was not different between obstacles, but obstacle one was still perceived as 2.4 cm larger. Conclusions/Significance There was evidence of locomotor adaptation, but no evidence of perceptual adaptation with experience. These findings add to research that demonstrates that while the motor system can be influenced by perception, it can also operate independent of perception

Estimating the incidence of lung cancer attributable to occupational exposure in Iran

<p>Abstract</p> <p>Objective</p> <p>The aim of this study was to estimate the fraction of lung cancer incidence in Iran attributed to occupational exposures to the well-established lung cancer carcinogens, including silica, cadmium, nickel, arsenic, chromium, diesel fumes, beryllium, and asbestos.</p> <p>Methods</p> <p>Nationwide exposure to each of the mentioned carcinogens was estimated using workforce data from the Iranian population census of 1995, available from the International Labor Organization (ILO) website. The prevalence of exposure to carcinogens in each industry was estimated using exposure data from the CAREX (CARcinogen EXposure) database, an international occupational carcinogen information system kept and maintained by the European Union. The magnitude of the relative risk of lung cancer for each carcinogen was estimated from local and international literature. Using the Levin modified population attributable risk (incidence) fraction, lung cancer incidence (as estimated by the Tehran Population-Based Cancer Registry) attributable to workplace exposure to carcinogens was estimated.</p> <p>Results</p> <p>The total workforce in Iran according to the 1995 census identified 12,488,020 men and 677,469 women. Agriculture is the largest sector with 25% of the male and 0.27% of female workforce. After applying the CAREX exposure estimate to each sector, the proportion exposed to lung carcinogens was 0.08% for male workers and 0.02% for female workers. Estimating a relative risk of 1.9 (95% CI of 1.7–2.1) for high exposure and 1.3 (95% CI 1.2–1.4) for low exposure, and employing the Levin modified formula, the fraction of lung cancer attributed to carcinogens in the workplace was 1.5% (95% CI of 1.2–1.9) for females and 12% (95% CI of 10–15) for males. These fractions correspond to an estimated incidence of 1.3 and 0.08 cases of lung cancer per 100,000 population for males and females, respectively.</p> <p>Conclusion</p> <p>The incidence of lung cancer due to occupational exposure is low in Iran and, as in other countries, more lung cancer is due to occupational exposure among males than females.</p

When Is Visual Information Used to Control Locomotion When Descending a Kerb?

YesBackground: Descending kerbs during locomotion involves the regulation of appropriate foot placement before the kerb-edge and foot clearance over it. It also involves the modulation of gait output to ensure the body-mass is safely and smoothly lowered to the new level. Previous research has shown that vision is used in such adaptive gait tasks for feedforward planning, with vision from the lower visual field (lvf) used for online updating. The present study determined when lvf information is used to control/update locomotion when stepping from a kerb. Methodology/Principal Findings: 12 young adults stepped down a kerb during ongoing gait. Force sensitive resistors (attached to participants' feet) interfaced with an high-speed PDLC 'smart glass' sheet, allowed the lvf to be unpredictably occluded at either heel-contact of the penultimate or final step before the kerb-edge up to contact with the lower level. Analysis focussed on determining changes in foot placement distance before the kerb-edge, clearance over it, and in kinematic measures of the step down. Lvf occlusion from the instant of final step contact had no significant effect on any dependant variable (p>0.09). Occlusion of the lvf from the instant of penultimate step contact had a significant effect on foot clearance and on several kinematic measures, with findings consistent with participants becoming uncertain regarding relative horizontal location of the kerb-edge. Conclusion/Significance: These findings suggest concurrent feedback of the lower limb, kerb-edge, and/or floor area immediately in front/below the kerb is not used when stepping from a kerb during ongoing gait. Instead heel-clearance and pre-landing-kinematic parameters are determined/planned using lvf information acquired in the penultimate step during the approach to the kerb-edge, with information related to foot placement before the kerb-edge being the most salient

Disclosure of cancer diagnosis and quality of life in cancer patients: should it be the same everywhere?

<p>Abstract</p> <p>Background</p> <p>Evidence suggests that truth telling and honest disclosure of cancer diagnosis could lead to improved outcomes in cancer patients. To examine such findings in Iran, this trial aimed to study the various dimensions of quality of life in patients with gastrointestinal cancer and to compare these variables among those who knew their diagnosis and those who did not.</p> <p>Methods</p> <p>A consecutive sample of patients with gastrointestinal cancer being treated in Cancer Institute in Tehran, Iran was prospectively evaluated. A psychologist interviewed patients using the Iranian version of the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30). Patients were categorized into two groups: those who knew their diagnosis and those who did not. Independent sample t-test was used for group comparisons.</p> <p>Results</p> <p>In all 142 patients were interviewed. A significant proportion (52%) of patients did not know their cancer diagnosis and 48% of patients were aware that they had cancer. They were quite similar in most characteristics. The comparison of quality of life between two groups indicated that those knew their diagnosis showed a significant lower degree of physical (P = 0.001), emotional (P = 0.01) and social functioning (P < 0.001), whereas the global quality of life and other functional scales including role functioning and cognitive functioning did not show significant result. There were no statistically significant differences between symptoms scores between two groups, except for fatigue suggesting a higher score in patients who knew their diagnosis (P = 0.01). The financial difficulties were also significantly higher in patients who knew their cancer diagnosis (P = 0.005). Performing analysis of variance while controlling for age, educational status, cancer site, and knowledge of cancer diagnosis, the results showed that the knowledge of cancer diagnosis independently still contributed to the significant differences observed between two groups.</p> <p>Conclusion</p> <p>Contrary to expectation the findings indicated that patients who did not know their cancer diagnosis had a better physical, social and emotional quality of life. It seems that due to cultural differences between countries cancer disclosure guidelines perhaps should be differing.</p

Considerations about quality in model-driven engineering

The final publication is available at Springer via http://dx.doi.org/10.1007/s11219-016-9350-6The virtue of quality is not itself a subject; it depends on a subject. In the software engineering field, quality means good software products that meet customer expectations, constraints, and requirements. Despite the numerous approaches, methods, descriptive models, and tools, that have been developed, a level of consensus has been reached by software practitioners. However, in the model-driven engineering (MDE) field, which has emerged from software engineering paradigms, quality continues to be a great challenge since the subject is not fully defined. The use of models alone is not enough to manage all of the quality issues at the modeling language level. In this work, we present the current state and some relevant considerations regarding quality in MDE, by identifying current categories in quality conception and by highlighting quality issues in real applications of the model-driven initiatives. We identified 16 categories in the definition of quality in MDE. From this identification, by applying an adaptive sampling approach, we discovered the five most influential authors for the works that propose definitions of quality. These include (in order): the OMG standards (e.g., MDA, UML, MOF, OCL, SysML), the ISO standards for software quality models (e.g., 9126 and 25,000), Krogstie, Lindland, and Moody. We also discovered families of works about quality, i.e., works that belong to the same author or topic. Seventy-three works were found with evidence of the mismatch between the academic/research field of quality evaluation of modeling languages and actual MDE practice in industry. We demonstrate that this field does not currently solve quality issues reported in industrial scenarios. The evidence of the mismatch was grouped in eight categories, four for academic/research evidence and four for industrial reports. These categories were detected based on the scope proposed in each one of the academic/research works and from the questions and issues raised by real practitioners. We then proposed a scenario to illustrate quality issues in a real information system project in which multiple modeling languages were used. For the evaluation of the quality of this MDE scenario, we chose one of the most cited and influential quality frameworks; it was detected from the information obtained in the identification of the categories about quality definition for MDE. We demonstrated that the selected framework falls short in addressing the quality issues. Finally, based on the findings, we derive eight challenges for quality evaluation in MDE projects that current quality initiatives do not address sufficiently.F.G, would like to thank COLCIENCIAS (Colombia) for funding this work through the Colciencias Grant call 512-2010. This work has been supported by the Gene-ralitat Valenciana Project IDEO (PROMETEOII/2014/039), the European Commission FP7 Project CaaS (611351), and ERDF structural funds.Giraldo-Velásquez, FD.; España Cubillo, S.; Pastor López, O.; Giraldo, WJ. (2016). Considerations about quality in model-driven engineering. Software Quality Journal. 1-66. https://doi.org/10.1007/s11219-016-9350-6S166(1985). Iso information processing—documentation symbols and conventions for data, program and system flowcharts, program network charts and system resources charts. ISO 5807:1985(E) (pp. 1–25).(2011). Iso/iec/ieee systems and software engineering – architecture description. ISO/IEC/IEEE 42010:2011(E) (Revision of ISO/IEC 42010:2007 and IEEE Std 1471-2000) (pp. 1–46).Abran, A., Moore, J.W., Bourque, P., Dupuis, R., & Tripp, L.L. (2013). Guide to the Software Engineering Body of Knowledge (SWEBOK) version 3 public review. IEEE. ISO Technical Report ISO/IEC TR 19759.Agner, L.T.W., Soares, I.W., Stadzisz, P.C., & Simão, J.M. (2013). A brazilian survey on {UML} and model-driven practices for embedded software development. Journal of Systems and Software, 86(4), 997–1005. {SI} : Software Engineering in Brazil: Retrospective and Prospective Views.Amstel, M.F.V. (2010). The right tool for the right job: assessing model transformation quality. pages 69–74. Affiliation: Eindhoven University of Technology, P.O. Box 513, 5600 MB, Eindhoven, Netherlands. Cited By (since 1996):1.Aranda, J., Damian, D., & Borici, A. (2012). Transition to model-driven engineering: what is revolutionary, what remains the same?. In Proceedings of the 15th international conference on model driven engineering languages and systems, MODELS’12 (pp. 692–708). Berlin, Heidelberg: Springer.Arendt, T., & Taentzer, G. (2013). A tool environment for quality assurance based on the eclipse modeling framework. Automated Software Engineering, 20(2), 141–184.Atkinson, C., Bunse, C., & Wüst, J. (2003). Driving component-based software development through quality modelling, volume 2693. Cited By (since 1996):3.Baker, P., Loh, S., & Weil, F. (2005). Model-driven engineering in a large industrial context—motorola case study. In Briand, L., & Williams, C. (Eds.) Model Driven Engineering Languages and Systems, volume 3713 of Lecture Notes in Computer Science (pp. 476–491). Berlin, Heidelberg: Springer.Barišić, A., Amaral, V., Goulão, M., & Barroca, B. (2011). Quality in use of domain-specific languages: a case study. In Proceedings of the 3rd ACM SIGPLAN workshop on evaluation and usability of programming languages and tools, PLATEAU ’11 (pp. 65–72). New York: ACM.Becker, J., Bergener, P., Breuker, D., & Rackers, M. (2010). Evaluating the expressiveness of domain specific modeling languages using the bunge-wand-weber ontology. In 2010 43rd Hawaii international conference on system sciences (HICSS) (pp. 1–10).Bertrand Portier, L.A. (2009). Model driven development misperceptions and challenges.Bézivin, J., & Kurtev, I. (2005). Model-based technology integration with the technical space concept. In Proceedings of the Metainformatics Symposium: Springer.Brambilla, M. (2016). How mature is of model-driven engineering as an engineering discipline @ONLINE.Brambilla, M., & Fraternali, P. (2014). Large-scale model-driven engineering of web user interaction: The webml and webratio experience. Science of Computer Programming, 89 Part B(0), 71 – 87. Special issue on Success Stories in Model Driven Engineering.Brown, A. (2009). Simple and practical model driven architecture (mda) @ONLINE.Bruel, J.-M., Combemale, B., Ober, I., & Raynal, H. (2015). Mde in practice for computational science. Procedia Computer Science, 51, 660–669.Budgen, D., Burn, A.J., Brereton, O.P., Kitchenham, B.A., & Pretorius, R. (2011). Empirical evidence about the uml: a systematic literature review. Software: Practice and Experience, 41(4), 363–392.Burden, H., Heldal, R., & Whittle, J. (2014). Comparing and contrasting model-driven engineering at three large companies. In Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM ’14 (pp. 14:1–14:10). New York: ACM.Cabot, J. Has mda been abandoned (by the omg)?Cabot, J. (2009). Modeling will be commonplace in three years time @ONLINE.Cachero, C., Poels, G., Calero, C., & Marhuenda, Y. (2007). Towards a Quality-Aware Engineering Process for the Development of Web Applications. Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/462, Ghent University, Faculty of Economics and Business Administration.Challenger, M., Kardas, G., & Tekinerdogan, B. (2015). A systematic approach to evaluating domain-specific modeling language environments for multi-agent systems. Software Quality Journal, 1–41.Chaudron, M.V., Heijstek, W., & Nugroho, A. (2012). How effective is uml modeling? Software & Systems Modeling, 11(4), 571–580. J2: Softw Syst Model.Chenouard, R., Granvilliers, L., & Soto, R. (2008). Model-driven constraint programming. pages 236–246. Affiliation: CNRS, LINA, Universit de Nantes, France; Affiliation: Pontificia Universidad Catlica de, Valparaiso, Chile. Cited By (since 1996):8.Clark, T., & Muller, P.-A. (2012). Exploiting model driven technology: a tale of two startups. Software and Systems Modeling, 11(4), 481–493.Corneliussen, L. (2008). What do you think of model-driven software development?Costal, D., Gómez, C., & Guizzardi, G. (2011). Formal semantics and ontological analysis for understanding subsetting, specialization and redefinition of associations in uml. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6998 LNCS:189–203. cited By (since 1996)3.Cruz-Lemus, J.A., Maes, A., Género, M., Poels, G., & Piattini, M. (2010). The impact of structural complexity on the understandability of uml statechart diagrams. Information Sciences, 180(11), 2209–2220. Cited By (since 1996):14.Cuadrado, J.S., Izquierdo, J.L.C., & Molina, J.G. (2014). Applying model-driven engineering in small software enterprises. Science of Computer Programming, 89 Part B(0), 176 – 198. Special issue on Success Stories in Model Driven Engineering.Da Silva, A.R. (2015). Model-driven engineering: a survey supported by the unified conceptual model. Computer Languages Systems and Structures, 43, 139–155.Da Silva Teixeira, D.G.M., Quirino, G.K., Gailly, F., De Almeida Falbo, R., Guizzardi, G., & Perini Barcellos, M. (2016). PoN-S: a Systematic Approach for Applying the Physics of Notation (PoN), (pp. 432–447). Cham: Springer International Publishing.Davies, I., Green, P., Rosemann, M., Indulska, M., & Gallo, S. (2006). How do practitioners use conceptual modeling in practice? Data and Knowledge Engineering, 58(3), 358 – 380. Including the special issue : {ER} 2004ER 2004.Davies, J., Milward, D., Wang, C.-W., & Welch, J. (2015). Formal model-driven engineering of critical information systems. Science of Computer Programming, 103(0), 88 – 113. Selected papers from the First International Workshop on Formal Techniques for Safety-Critical Systems (FTSCS 2012).De Oca, I.M.-M., Snoeck, M., Reijers, H.A., & Rodríguez-Morffi, A. (2015). A systematic literature review of studies on business process modeling quality. Information and Software Technology, 58, 187–205.DenHaan, J. (2009). 8 reasons why model driven development is dangerous @ONLINE.DenHaan, J. (2010). Model driven engineering vs the commando pattern @ONLINE.DenHaan, J. (2011a). Why aren’t we all doing model driven development yet @ONLINE.DenHaan, J. (2011b). Why there is no future model driven development @ONLINE.Di Ruscio, D., Iovino, L., & Pierantonio, A. (2013). Managing the coupled evolution of metamodels and textual concrete syntax specifications. cited By (since 1996)0.Dijkman, R.M., Dumas, M., & Ouyang, C. (2008). Semantics and analysis of business process models in {BPMN}. Information and Software Technology, 50(12), 1281–1294.Domínguez-Mayo, F.J., Escalona, M.J., Mejías, M., Ramos, I., & Fernández, L. (2011). A framework for the quality evaluation of mdwe methodologies and information technology infrastructures. International Journal of Human Capital and Information Technology Professionals, 2(4), 11–22.Domínguez-Mayo, F.J., Escalona, M.J., Mejías, M., & Torres, A.H. (2010). A quality model in a quality evaluation framework for mdwe methodologies. pages 495–506. Affiliation: Departamento de Lenguajes y Sistemas Informíticos, University of Seville, Seville, Spain., Cited By (since 1996):1.Dubray, J.-J. (2011). Why did mde miss the boat?.Escalona, M.J., Gutiérrez, J.J., Pérez-Pérez, M., Molina, A., Domínguez-Mayo, E., & Domínguez-Mayo, F.J. (2011). Measuring the Quality of Model-Driven Projects with NDT-Quality, (pp. 307–317). New York: Springer.Espinilla, M., Domínguez-Mayo, F.J., Escalona, M.J., Mejías, M., Ross, M., & Staples, G. (2011). A Method Based on AHP to Define the Quality Model of QuEF (Vol. 123, pp. 685–694). Berlin, Heidelberg: Springer.Fabra, J., Castro, V.D., Álvarez, P., & Marcos, E. (2012). Automatic execution of business process models: exploiting the benefits of model-driven engineering approaches. Journal of Systems and Software, 85(3), 607–625. Novel approaches in the design and implementation of systems/software architecture.Falkenberg, E.D., Hesse, W., Lindgreen, P., Nilsson, B.E., Oei, J.L.H., Rolland, C., Stamper, R.K., Assche, F.J.M.V., Verrijn-Stuart, A.A., & Voss, K. (1996). Frisco: a framework of information system concepts. Technical report, The IFIP WG 8. 1 Task Group FRISCO.Fettke, P., Houy, C., Vella, A.-L., & Loos, P. (2012). Towards the Reconstruction and Evaluation of Conceptual Model Quality Discourses – Methodical Framework and Application in the Context of Model Understandability, volume 113 of Lecture Notes in Business Information Processing, chapter 28, pages 406–421, Springer, Berlin, Heidelberg.Finnie, S. (2015). Modeling community: Are we missing something?Fournier, C. (2008). Is uml [email protected], R., & Rumpe, B. (2007). Model-driven development of complex software: a research roadmap. In Future of Software Engineering, 2007, FOSE ’07 (pp. 37–54).Gallego, M., Giraldo, F.D., & Hitpass, B. (2015). Adapting the pbec-otss software selection approach for bpm suites: an application case. In 2015 34th International Conference of the Chilean Computer Science Society (SCCC) (pp. 1–10).Galvão, I., & Goknil, A. (2007). Survey of traceability approaches in model-driven engineering. cited By (since 1996)22.Giraldo, F., España, S., Giraldo, W., & Pastor, O. (2015). Modelling language quality evaluation in model-driven information systems engineering: a roadmap. In 2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS) (pp. 64–69).Giraldo, F., España, S., & Pastor, O. (2014). Analysing the concept of quality in model-driven engineering literature: a systematic review. In 2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS) (pp. 1–12).Giraldo, F.D., España, S., & Pastor, O. (2016). Evidences of the mismatch between industry and academy on modelling language quality evaluation. arXiv: 1606.02025 .González, C., & Cabot, J. (2014). Formal verification of static software models in mde: a systematic review. Information and Software Technology, 56(8), 821–838. cited By (since 1996)0.González, C.A., Büttner, F., Clarisó, R., & Cabot, J. (2012). Emftocsp: a tool for the lightweight verification of emf models. pages 44–50. Affiliation: cole des Mines de Nantes, INRIA, LINA, Nantes, France; Affiliation: Universitat Oberta de Catalunya, Barcelona, Spain. Cited By (since 1996):1.Gorschek, T., Tempero, E., & Angelis, L. (2014). On the use of software design models in software development practice: an empirical investigation. Journal of Systems and Software, 95(0), 176– 193.Goulão, M., Amaral, V., & Mernik, M. (2016). Quality in model-driven engineering: a tertiary study. Software Quality Journal, 1–33.Grobshtein, Y., & Dori, D. (2011). Generating sysml views from an opm model: design and evaluation. Systems Engineering, 14(3), 327–340.Haan, J.d. (2008). 8 reasons why model-driven approaches (will) fail.Harel, D., & Rumpe, B. (2000). Modeling languages: Syntax, semantics and all that stuff, part i: The basic stuff, Israel. Technical report Jerusalem Israel.Harel, D., & Rumpe, B. (2004). Meaningful modeling: what’s the semantics of semantics? Computer, 37(10), 64–72.Hebig, R., & Bendraou, R. (2014). On the need to study the impact of model driven engineering on software processes. In Proceedings of the 2014 International Conference on Software and System Process, ICSSP 2014 (pp. 164–168). New York: ACM.Heidari, F., & Loucopoulos, P. (2014). Quality evaluation framework (qef): modeling and evaluating quality of business processes. International Journal of Accounting Information Systems, 15(3), 193–223. Business Process Modeling.Heymans, P., Schobbens, P.Y., Trigaux, J.C., Bontemps, Y., Matulevicius, R., & Classen, A. (2008). Evaluating formal properties of feature diagram languages. Software, IET, 2(3), 281–302. ID 2.Hindawi, M., Morel, L., Aubry, R., & Sourrouille, J.-L. (2009). Description and Implementation of a UML Style Guide (Vol. 5421, pp. 291–302). Berlin: Springer.Hoang, D. (2012). Current limitations of mdd and its implications @ONLINE.Hodges, W. (2013). Model theory Zalta, E.N. (Ed.) The Stanford Encyclopedia of Philosophy. Fall 2013 edition.Hutchinson, J., Rouncefield, M., & Whittle, J. (2011a). Model-driven engineering practices in industry. In Proceedings of the 33rd International Conference on Software Engineering, ICSE’11 (pp. 633–642). New York: ACM.Hutchinson, J., Whittle, J., & Rouncefield, M. (2014). Model-driven engineering practices in industry: social, organizational and managerial factors that lead to success or failure. Science of Computer Programming, 89 Part B(0), 144–161. Special issue on Success Stories in Model Driven Engineering.Hutchinson, J., Whittle, J., Rouncefield, M., & Kristoffersen, S. (2011b). Empirical assessment of mde in industry. In Proceedings of the 33rd International Conference on Software Engineering, ICSE’11 (pp. 471–480). New York: ACM.Igarza, I.M.H., Boada, D.H.G., & Valdés, A.P. (2012). Una introducción al desarrollo de software dirigido por modelos. Serie Científica, 5(3).ISO/IEC (2001). ISO/IEC 9126. Software engineering—Product quality. ISO/IEC.Izurieta, C., Rojas, G., & Griffith, I. (2015). Preemptive management of model driven technical debt for improving software quality. In Proceedings of the 11th International ACM SIGSOFT Conference on Quality of Software Architectures, QoSA’15 (pp. 31–36). New York: ACM.Jalali, S., & Wohlin, C. (2012). Systematic literature studies: Database searches vs. backward snowballing. In Proceedings of the ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM’12 (pp. 29–38). New York: ACM.Kahraman, G., & Bilgen, S. (2013). A framework for qualitative assessment of domain-specific languages. Software & Systems Modeling, 1–22.Kessentini, M., Langer, P., & Wimmer, M. (2013). Searching models, modeling search: On the synergies of sbse and mde (pp. 51–54).Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature Reviews in Software Engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report.Kitchenham, B., Pfleeger, S., Pickard, L., Jones, P., Hoaglin, D., El Emam, K., & Rosenberg, J. (2002). Preliminary guidelines for empirical research in software engineering. IEEE Transactions on Software Engineering, 28(8), 721–734.Klinke, M. (2008). Do you use mda/mdd/mdsd, any kind of model-driven approach? Will it be the future?Köhnlein, J. (2013). Eclipse diagram editors from a user’s perspective.Kolovos, D.S., Paige, R.F., & Polack, F.A. (2008). The grand challenge of scalability for model driven engineering. In Models in Software Engineering (pp. 48–53): Springer.Kolovos, D.S., Rose, L.M., Matragkas, N., Paige, R.F., Guerra, E., Cuadrado, J.S., De Lara, J., Ráth, I., Varró, D., Tisi, M., & Cabot, J. (2013). A research roadmap towards achieving scalability in model driven engineering. In Proceedings of the Workshop on Scalability in Model Driven Engineering, BigMDE’13 (pp. 2:1–2:10). New York: ACM.Krill, P. (2016). Uml to be ejected from microsoft visual studio (infoworld).Krogstie, J. (2012a). Model-based development and evolution of information systems: a quality approach, Springer Publishing Company, Incorporated.Krogstie, J. (2012b). Quality of modelling languages, (pp. 249–280). London: Springer.Krogstie, J. (2012c). Quality of models, (pp. 205–247). London: Springer.Krogstie, J. (2012d). Specialisations of SEQUAL, (pp. 281–326). London: Springer.Krogstie, J., Lindland, O.I., & Sindre, G. (1995). Defining quality aspects for conceptual models. In Proceedings of the IFIP International Working Conference on Information System Concepts: Towards a Consolidation of Views (pp. 216–231). London: Chapman & Hall, Ltd.Kruchten, P. (2000). The rational unified process: an introduction, 2nd edn. Boston: Addison-Wesley Longman Publishing Co., Inc.Kruchten, P., Nord, R., & Ozkaya, I. (2012). Technical debt: from metaphor to theory and practice. Software, IEEE, 29(6), 18–21.Kulkarni, V., Reddy, S., & Rajbhoj, A. (2010). Scaling up model driven engineering – experience and lessons learnt. In Petriu, D., Rouquette, N., & Haugen, y. (Eds.) Model Driven Engineering Languages and Systems, volume 6395 of Lecture Notes in Computer Science (pp. 331–345). Berlin, Heidelberg: Springer.Laguna, M.A., & Marqués, J.M. (2010). Uml support for designing software product lines: the package merge mechanism, 16(17), 2313–2332.Lange, C. (2007a). Model size matters. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4364 LNCS:211–216. cited By (since 1996)1.Lange, C., & Chaudron, M. (2005). Managing Model Quality in UML-Based Software Development. In 13th IEEE International Workshop on Technology and Engineering Practice, 2005 (pp. 7–16).Lange, C., Chaudron, M.R.V., Muskens, J., Somers, L.J., & Dortmans, H.M. (2003). An empirical investigation in quantifying inconsistency and incompleteness of uml designs. In Incompleteness of UML Designs, Proceedings Workshop on Consistency Problems in UML-based Software Development, 6th International Conference on Unified Modeling Language, UML, 2003.Lange, C., DuBois, B., Chaudron, M., & Demeyer, S. (2006). An experimental investigation of uml modeling conventions. In Nierstrasz, O., Whittle, J., Harel, D., & Reggio, G. (Eds.) Model Driven Engineering Languages and Systems, volume 4199 of Lecture Notes in Computer Science (pp. 27–41). Berlin, Heidelberg: Springer.Lange, C.F.J., & Chaudron, M.R.V. (2006). Effe

Evaluation of Cause of Deaths' Validity Using Outcome Measures from a Prospective, Population Based Cohort Study in Tehran, Iran

OBJECTIVE: The aim of this study was to evaluate the validity of cause of death stated in death certificates in Tehran using outcome measures of the Tehran Lipid and Glucose Study (TLGS), an ongoing prospective cohort study. METHODS: The cohort was established in 1999 in a population of 15005 people, 3 years old and over, living in Tehran; 3551 individuals were added to this population three years later. As part of cohort's outcome measures, deaths occurring in the cohort are investigated by a panel of medical specialists (Cohort Outcome Panel--COP) and underlying cause of death is determined for each death. The cause of death assigned in a deceased's original death certificate was evaluated against the cause of death determined by COP and sensitivity and positive predictive values (PPV) were determined. In addition, determinants of assigning accurate underlying cause of death were determined using logistic regression model. RESULT: A total of 231 death certificates were evaluated. The original death certificates over reported deaths due to neoplasms and underreported death due to circulatory system and transport accidents. Neoplasms with sensitivity of 0.91 and PPV of 0.71 were the most valid category. The disease of circulatory system showed moderate degree of validity with sensitivity of 0.67 and PPV of 0.78. The result of logistic regression indicated if the death certificate is issued by a general practitioner, there is 2.3 (95% CI 1.1, 5.1) times chance of being misclassified compared with when it is issued by a specialist. If the deceased is more than 60 years, the chance of misclassification would be 2.5 times (95% CI of 1.1, 5.9) compared with when the deceased is less than 60 years

Diagnostic Accuracy of Age and Alarm Symptoms for Upper GI Malignancy in Patients with Dyspepsia in a GI Clinic: A 7-Year Cross-Sectional Study

<div><h3>Objectives</h3><p>We investigated whether using demographic characteristics and alarm symptoms can accurately predict cancer in patients with dyspepsia in Iran, where upper GI cancers and <em>H. pylori</em> infection are common.</p> <h3>Methods</h3><p>All consecutive patients referred to a tertiary gastroenterology clinic in Tehran, Iran, from 2002 to 2009 were invited to participate in this study. Each patient completed a standard questionnaire and underwent upper gastrointestinal endoscopy. Alarm symptoms included in the questionnaire were weight loss, dysphagia, GI bleeding, and persistent vomiting. We used logistic regression models to estimate the diagnostic value of each variable in combination with other ones, and to develop a risk-prediction model.</p> <h3>Results</h3><p>A total of 2,847 patients with dyspepsia participated in this study, of whom 87 (3.1%) had upper GI malignancy. Patients reporting at least one of the alarm symptoms constituted 66.7% of cancer patients compared to 38.9% in patients without cancer (p<0.001). Esophageal or gastric cancers in patients with dyspepsia was associated with older age, being male, and symptoms of weight loss and vomiting. Each single predictor had low sensitivity and specificity. Using a combination of age, alarm symptoms, and smoking, we built a risk-prediction model that distinguished between high-risk and low-risk individuals with an area under the ROC curve of 0.85 and acceptable calibration.</p> <h3>Conclusions</h3><p>None of the predictors demonstrated high diagnostic accuracy. While our risk-prediction model had reasonable accuracy, some cancer cases would have remained undiagnosed. Therefore, where available, low cost endoscopy may be preferable for dyspeptic older patient or those with history of weight loss.</p> </div

Hot or not? Discovery and characterization of a thermostable alditol oxidase from Acidothermus cellulolyticus 11B

We describe the discovery, isolation and characterization of a highly thermostable alditol oxidase from Acidothermus cellulolyticus 11B. This protein was identified by searching the genomes of known thermophiles for enzymes homologous to Streptomyces coelicolor A3(2) alditol oxidase (AldO). A gene (sharing 48% protein sequence identity to AldO) was identified, cloned and expressed in Escherichia coli. Following 6xHis tag purification, characterization revealed the protein to be a covalent flavoprotein of 47 kDa with a remarkably similar reactivity and substrate specificity to that of AldO. A steady-state kinetic analysis with a number of different polyol substrates revealed lower catalytic rates but slightly altered substrate specificity when compared to AldO. Thermostability measurements revealed that the novel AldO is a highly thermostable enzyme with an unfolding temperature of 84 °C and an activity half-life at 75 °C of 112 min, prompting the name HotAldO. Inspired by earlier studies, we attempted a straightforward, exploratory approach to improve the thermostability of AldO by replacing residues with high B-factors with corresponding residues from HotAldO. None of these mutations resulted in a more thermostable oxidase; a fact that was corroborated by in silico analysis