463 research outputs found

    Regulation of MMP2 and MMP9 metalloproteinases by FSH and growth factors in bovine granulosa cells

    Get PDF
    Matrix metalloproteinases (MMP) are key enzymes involved in tissue remodeling. Within the ovary, they are believed to play a major role in ovulation, and have been linked to follicle atresia. To gain insight into the regulation of MMPs, we measured the effect of hormones and growth factors on MMP2 and MMP9 mRNA levels in non-luteinizing granulosa cells in serum-free culture. FSH and IGF1 both stimulated estradiol secretion and inhibited MMP2 and MMP9 mRNA abundance. In contrast, EGF and FGF2 both inhibited estradiol secretion but had no effect on MMP expression. At physiological doses, none of these hormones altered the proportion of dead cells. Although we cannot link MMP expression with apoptosis, the specific down regulation by the gonadotropic hormones FSH and IGF1 in vitro suggests that excess MMP2 and MMP9 expression is neither required nor desired for follicle development

    Observation and study of baryonic B decays: B -> D(*) p pbar, D(*) p pbar pi, and D(*) p pbar pi pi

    Get PDF
    We present a study of ten B-meson decays to a D(*), a proton-antiproton pair, and a system of up to two pions using BaBar's data set of 455x10^6 BBbar pairs. Four of the modes (B0bar -> D0 p anti-p, B0bar -> D*0 p anti-p, B0bar -> D+ p anti-p pi-, B0bar -> D*+ p anti-p pi-) are studied with improved statistics compared to previous measurements; six of the modes (B- -> D0 p anti-p pi-, B- -> D*0 p anti-p pi-, B0bar -> D0 p anti-p pi- pi+, B0bar -> D*0 p anti-p pi- pi+, B- -> D+ p anti-p pi- pi-, B- -> D*+ p anti-p pi- pi-) are first observations. The branching fractions for 3- and 5-body decays are suppressed compared to 4-body decays. Kinematic distributions for 3-body decays show non-overlapping threshold enhancements in m(p anti-p) and m(D(*)0 p) in the Dalitz plots. For 4-body decays, m(p pi-) mass projections show a narrow peak with mass and full width of (1497.4 +- 3.0 +- 0.9) MeV/c2, and (47 +- 12 +- 4) MeV/c2, respectively, where the first (second) errors are statistical (systematic). For 5-body decays, mass projections are similar to phase space expectations. All results are preliminary.Comment: 28 pages, 90 postscript figures, submitted to LP0

    Search for rare quark-annihilation decays, B --> Ds(*) Phi

    Full text link
    We report on searches for B- --> Ds- Phi and B- --> Ds*- Phi. In the context of the Standard Model, these decays are expected to be highly suppressed since they proceed through annihilation of the b and u-bar quarks in the B- meson. Our results are based on 234 million Upsilon(4S) --> B Bbar decays collected with the BABAR detector at SLAC. We find no evidence for these decays, and we set Bayesian 90% confidence level upper limits on the branching fractions BF(B- --> Ds- Phi) Ds*- Phi)<1.2x10^(-5). These results are consistent with Standard Model expectations.Comment: 8 pages, 3 postscript figues, submitted to Phys. Rev. D (Rapid Communications

    Flow shop rescheduling under different types of disruption

    Full text link
    This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 2013, available online:http://www.tandfonline.com/10.1080/00207543.2012.666856Almost all manufacturing facilities need to use production planning and scheduling systems to increase productivity and to reduce production costs. Real-life production operations are subject to a large number of unexpected disruptions that may invalidate the original schedules. In these cases, rescheduling is essential to minimise the impact on the performance of the system. In this work we consider flow shop layouts that have seldom been studied in the rescheduling literature. We generate and employ three types of disruption that interrupt the original schedules simultaneously. We develop rescheduling algorithms to finally accomplish the twofold objective of establishing a standard framework on the one hand, and proposing rescheduling methods that seek a good trade-off between schedule quality and stability on the other.The authors would like to thank the anonymous referees for their careful and detailed comments that helped to improve the paper considerably. This work is partially financed by the Small and Medium Industry of the Generalitat Valenciana (IMPIVA) and by the European Union through the European Regional Development Fund (FEDER) inside the R + D program "Ayudas dirigidas a Institutos tecnologicos de la Red IMPIVA" during the year 2011, with project number IMDEEA/2011/142.Katragjini Prifti, K.; Vallada Regalado, E.; Ruiz García, R. (2013). Flow shop rescheduling under different types of disruption. International Journal of Production Research. 51(3):780-797. https://doi.org/10.1080/00207543.2012.666856S780797513Abumaizar, R. J., & Svestka, J. A. (1997). Rescheduling job shops under random disruptions. International Journal of Production Research, 35(7), 2065-2082. doi:10.1080/002075497195074Adiri, I., Frostig, E., & Kan, A. H. G. R. (1991). Scheduling on a single machine with a single breakdown to minimize stochastically the number of tardy jobs. Naval Research Logistics, 38(2), 261-271. doi:10.1002/1520-6750(199104)38:23.0.co;2-iAkturk, M. S., & Gorgulu, E. (1999). Match-up scheduling under a machine breakdown. European Journal of Operational Research, 112(1), 81-97. doi:10.1016/s0377-2217(97)00396-2Allahverdi, A. (1996). Two-machine proportionate flowshop scheduling with breakdowns to minimize maximum lateness. Computers & Operations Research, 23(10), 909-916. doi:10.1016/0305-0548(96)00012-3Arnaout, J. P., & Rabadi, G. (2008). Rescheduling of unrelated parallel machines under machine breakdowns. International Journal of Applied Management Science, 1(1), 75. doi:10.1504/ijams.2008.020040Artigues, C., Billaut, J.-C., & Esswein, C. (2005). Maximization of solution flexibility for robust shop scheduling. European Journal of Operational Research, 165(2), 314-328. doi:10.1016/j.ejor.2004.04.004Azizoglu, M., & Alagöz, O. (2005). Parallel-machine rescheduling with machine disruptions. IIE Transactions, 37(12), 1113-1118. doi:10.1080/07408170500288133Bean, J. C., Birge, J. R., Mittenthal, J., & Noon, C. E. (1991). Matchup Scheduling with Multiple Resources, Release Dates and Disruptions. Operations Research, 39(3), 470-483. doi:10.1287/opre.39.3.470Caricato, P., & Grieco, A. (2008). An online approach to dynamic rescheduling for production planning applications. International Journal of Production Research, 46(16), 4597-4617. doi:10.1080/00207540601136225CHURCH, L. K., & UZSOY, R. (1992). Analysis of periodic and event-driven rescheduling policies in dynamic shops. International Journal of Computer Integrated Manufacturing, 5(3), 153-163. doi:10.1080/09511929208944524Cowling, P., & Johansson, M. (2002). Using real time information for effective dynamic scheduling. European Journal of Operational Research, 139(2), 230-244. doi:10.1016/s0377-2217(01)00355-1Curry, J., & Peters *, B. (2005). Rescheduling parallel machines with stepwise increasing tardiness and machine assignment stability objectives. International Journal of Production Research, 43(15), 3231-3246. doi:10.1080/00207540500103953DUTTA, A. (1990). Reacting to Scheduling Exceptions in FMS Environments. IIE Transactions, 22(4), 300-314. doi:10.1080/07408179008964185Ghezail, F., Pierreval, H., & Hajri-Gabouj, S. (2010). Analysis of robustness in proactive scheduling: A graphical approach. Computers & Industrial Engineering, 58(2), 193-198. doi:10.1016/j.cie.2009.03.004Goren, S., & Sabuncuoglu, I. (2008). Robustness and stability measures for scheduling: single-machine environment. IIE Transactions, 40(1), 66-83. doi:10.1080/07408170701283198Hall, N. G., & Potts, C. N. (2004). Rescheduling for New Orders. Operations Research, 52(3), 440-453. doi:10.1287/opre.1030.0101Herrmann, J. W., Lee, C.-Y., & Snowdon, J. L. (1993). A Classification of Static Scheduling Problems. Complexity in Numerical Optimization, 203-253. doi:10.1142/9789814354363_0011Herroelen, W., & Leus, R. (2005). Project scheduling under uncertainty: Survey and research potentials. European Journal of Operational Research, 165(2), 289-306. doi:10.1016/j.ejor.2004.04.002Hozak, K., & Hill, J. A. (2009). Issues and opportunities regarding replanning and rescheduling frequencies. International Journal of Production Research, 47(18), 4955-4970. doi:10.1080/00207540802047106Huaccho Huatuco, L., Efstathiou, J., Calinescu, A., Sivadasan, S., & Kariuki, S. (2009). Comparing the impact of different rescheduling strategies on the entropic-related complexity of manufacturing systems. International Journal of Production Research, 47(15), 4305-4325. doi:10.1080/00207540701871036Jensen, M. T. (2003). Generating robust and flexible job shop schedules using genetic algorithms. IEEE Transactions on Evolutionary Computation, 7(3), 275-288. doi:10.1109/tevc.2003.810067King, J. R. (1976). The theory-practice gap in job-shop scheduling. Production Engineer, 55(3), 137. doi:10.1049/tpe.1976.0044Kopanos, G. M., Capón-García, E., Espuña,, A., & Puigjaner, L. (2008). Costs for Rescheduling Actions: A Critical Issue for Reducing the Gap between Scheduling Theory and Practice. Industrial & Engineering Chemistry Research, 47(22), 8785-8795. doi:10.1021/ie8005676Lee, C.-Y., Leung, J. Y.-T., & Yu, G. (2006). Two Machine Scheduling under Disruptions with Transportation Considerations. Journal of Scheduling, 9(1), 35-48. doi:10.1007/s10951-006-5592-7Li, Z., & Ierapetritou, M. (2008). Process scheduling under uncertainty: Review and challenges. Computers & Chemical Engineering, 32(4-5), 715-727. doi:10.1016/j.compchemeng.2007.03.001Liao, C. J., & Chen, W. J. (2004). Scheduling under machine breakdown in a continuous process industry. Computers & Operations Research, 31(3), 415-428. doi:10.1016/s0305-0548(02)00224-1Mehta, S. V. (1999). Predictable scheduling of a single machine subject to breakdowns. International Journal of Computer Integrated Manufacturing, 12(1), 15-38. doi:10.1080/095119299130443MUHLEMANN, A. P., LOCKETT, A. G., & FARN, C.-K. (1982). Job shop scheduling heuristics and frequency of scheduling. International Journal of Production Research, 20(2), 227-241. doi:10.1080/00207548208947763Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91-95. doi:10.1016/0305-0483(83)90088-9O’Donovan, R., Uzsoy, R., & McKay, K. N. (1999). Predictable scheduling of a single machine with breakdowns and sensitive jobs. International Journal of Production Research, 37(18), 4217-4233. doi:10.1080/002075499189745Özlen, M., & Azizoğlu, M. (2009). Generating all efficient solutions of a rescheduling problem on unrelated parallel machines. International Journal of Production Research, 47(19), 5245-5270. doi:10.1080/00207540802043998Pfeiffer, A., Kádár, B., & Monostori, L. (2007). Stability-oriented evaluation of rescheduling strategies, by using simulation. Computers in Industry, 58(7), 630-643. doi:10.1016/j.compind.2007.05.009Pierreval, H., & Durieux-Paris, S. (2007). Robust simulation with a base environmental scenario. European Journal of Operational Research, 182(2), 783-793. doi:10.1016/j.ejor.2006.07.045Damodaran, P., Hirani, N. S., & Gallego, M. C. V. (2009). Scheduling identical parallel batch processing machines to minimise makespan using genetic algorithms. European J. of Industrial Engineering, 3(2), 187. doi:10.1504/ejie.2009.023605Qi, X., Bard, J. F., & Yu, G. (2006). Disruption management for machine scheduling: The case of SPT schedules. International Journal of Production Economics, 103(1), 166-184. doi:10.1016/j.ijpe.2005.05.021Rangsaritratsamee, R., Ferrell, W. G., & Kurz, M. B. (2004). Dynamic rescheduling that simultaneously considers efficiency and stability. Computers & Industrial Engineering, 46(1), 1-15. doi:10.1016/j.cie.2003.09.007Ruiz, R., & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-2049. doi:10.1016/j.ejor.2005.12.009Sabuncuoglu, I., & Goren, S. (2009). Hedging production schedules against uncertainty in manufacturing environment with a review of robustness and stability research. International Journal of Computer Integrated Manufacturing, 22(2), 138-157. doi:10.1080/09511920802209033Sabuncuoglu, I., & Kizilisik, O. B. (2003). Reactive scheduling in a dynamic and stochastic FMS environment. International Journal of Production Research, 41(17), 4211-4231. doi:10.1080/0020754031000149202Salveson, M. E. (1952). On a Quantitative Method in Production Planning and Scheduling. Econometrica, 20(4), 554. doi:10.2307/1907643Samarghandi, H., & ElMekkawy, T. Y. (2011). An efficient hybrid algorithm for the two-machine no-wait flow shop problem with separable setup times and single server. European J. of Industrial Engineering, 5(2), 111. doi:10.1504/ejie.2011.039869Subramaniam *, V., Raheja, A. S., & Rama Bhupal Reddy, K. (2005). Reactive repair tool for job shop schedules. International Journal of Production Research, 43(1), 1-23. doi:10.1080/0020754042000270412Taillard, E. (1990). Some efficient heuristic methods for the flow shop sequencing problem. European Journal of Operational Research, 47(1), 65-74. doi:10.1016/0377-2217(90)90090-xTaillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2), 278-285. doi:10.1016/0377-2217(93)90182-mValente, J. M. S., & Schaller, J. E. (2010). Improved heuristics for the single machine scheduling problem with linear early and quadratic tardy penalties. European J. of Industrial Engineering, 4(1), 99. doi:10.1504/ejie.2010.029572Vallada, E., & Ruiz, R. (2010). Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem☆. Omega, 38(1-2), 57-67. doi:10.1016/j.omega.2009.04.002Vieira, G. E., Herrmann, J. W., & Lin, E. (2000). Predicting the performance of rescheduling strategies for parallel machine systems. Journal of Manufacturing Systems, 19(4), 256-266. doi:10.1016/s0278-6125(01)80005-4Vieira, G. E., Herrmann, J. W., & Lin, E. (2003). Journal of Scheduling, 6(1), 39-62. doi:10.1023/a:1022235519958Yang, J., & Yu, G. (2002). Journal of Combinatorial Optimization, 6(1), 17-33. doi:10.1023/a:1013333232691Zandieh, M., & Gholami, M. (2009). An immune algorithm for scheduling a hybrid flow shop with sequence-dependent setup times and machines with random breakdowns. International Journal of Production Research, 47(24), 6999-7027. doi:10.1080/0020754080240063

    The Development of Criminal Style in Adolescence and Young Adulthood: Separating the Lemmings from the Loners

    Get PDF
    Despite broad consensus that most juvenile crimes are committed with peers, many questions regarding developmental and individual differences in criminal style (i.e., co-offending vs. solo offending) remain unanswered. Using prospective 3-year longitudinal data from 937 14- to 17-year-old serious male offenders, the present study investigates whether youths tend to offend alone, in groups, or a combination of the two; whether these patterns change with age; and whether youths who engage in a particular style share distinguishing characteristics. Trajectory analyses examining criminal styles over age revealed that, while most youth evinced both types of offending, two distinct groups emerged: an increasingly solo offender trajectory (83%); and a mixed style offender trajectory (17%). Alternate analyses revealed (5.5%) exclusively solo offenders (i.e., only committed solo offenses over 3 years). There were no significant differences between groups in individuals’ reported number of friends, quality of friendships, or extraversion. However, the increasingly solo and exclusively solo offenders reported more psychosocial maturity, lower rates of anxiety, fewer psychopathic traits, less gang involvement and less self reported offending than mixed style offenders. Findings suggest that increasingly and exclusively solo offenders are not loners, as they are sometimes portrayed, and that exclusively solo offending during adolescence, while rare and previously misunderstood, may not be a risk factor in and of itself

    Regulated expression of matrix metalloproteinases, inflammatory mediators, and endometrial matrix remodeling by 17beta-estradiol in the immature rat uterus

    Get PDF
    <p>Abstract</p> <p>Background</p> <p>Administration of a single physiological dose of 17beta-estradiol (E2:40 microg/kg) to the ovariectomized immature rat rapidly induces uterine growth and remodeling. The response is characterized by changes in endometrial stromal architecture during an inflammatory-like response that likely involves activated matrix-metalloproteinases (MMPs). While estrogen is known as an inducer of endometrial growth, its role in specific expression of MMP family members in vivo is poorly characterized. E2-induced changes in MMP-2, -3, -7, and -9 mRNA and protein expression were analyzed to survey regulation along an extended time course 0-72 hours post-treatment. Because E2 effects inflammatory-like changes that may alter MMP expression, we assessed changes in tissue levels of TNF-alpha and MCP-1, and we utilized dexamethasone (600 microg/kg) to better understand the role of inflammation on matrix remodeling.</p> <p>Methods</p> <p>Ovariectomized 21 day-old female Sprague-Dawley rats were administered E2 and uterine tissues were extracted and prepared for transmission electron microscopy (TEM), mRNA extraction and real-time RT-PCR, protein extraction and Western blot, or gelatin zymography. In inhibitor studies, pretreatment compounds were administered prior to E2 and tissues were harvested at 4 hours post-hormone challenge.</p> <p>Results</p> <p>Using a novel TEM method to quantitatively assess changes in stromal collagen density, we show that E2-induced matrix remodeling is rapid in onset (< 1 hour) and leads to a 70% reduction in collagen density by 4 hours. Matrix remodeling is MMP-dependent, as pretreatment with batimastat ablates the hormone effect. MMP-3, -7, and -9 and inflammatory markers (TNF-alpha and MCP-1) are transiently upregulated with peak expression at 4 hours post-E2 treatment. MMP-2 expression is increased by E2 but highest expression and activity occur later in the response (48 hours). Dexamethasone inhibits E2-modulated changes in collagen density and expression of MMPs although these effects are variable. Dexamethasone upregulates MMP-3 mRNA but not protein levels, inhibiting E2-induced upregulation of MMP-7, and -9, and MCP-1 mRNA and protein but not inhibiting the hormone-induced increase in TNF-alpha mRNA.</p> <p>Conclusion</p> <p>The data demonstrate that E2-regulated endometrial remodeling is rapid in onset (<1 hour) and peak expression of MMPs and inflammatory mediators correlates temporally with the period of lowest stromal collagen density during uterine tissue hypertrophy.</p

    An overview of using small punch testing for mechanical characterization of MCrAlY bond coats

    Get PDF
    Considerable work has been carried out on overlay bond coats in the past several decades because of its excellent oxidation resistance and good adhesion between the top coat and superalloy substrate in the thermal barrier coating systems. Previous studies mainly focus on oxidation and diffusion behavior of these coatings. However, the mechanical behavior and the dominant fracture and deformation mechanisms of the overlay bond coats at different temperatures are still under investigation. Direct comparison between individual studies has not yet been achieved due to the fragmentary data on deposition processes, microstructure and, more apparently, the difficulty in accurately measuring the mechanical properties of thin coatings. One of the miniaturized specimen testing methods, small punch testing, appears to have the potential to provide such mechanical property measurements for thin coatings. The purpose of this paper is to give an overview of using small punch testing to evaluate material properties and to summarize the available mechanical properties that include the ductile-to-brittle transition and creep of MCrAlY bond coat alloys, in an attempt to understand the mechanical behavior of MCrAlY coatings over a broad temperature range
    corecore