Comparison of algorithms for incoming atmospheric long-wave radiation

While numerous algorithms exist for predicting incident atmospheric long-wave radiation under clear (Lclr) and cloudy skies, few comparisons have been published to assess the accuracy of the different algorithms. Virtually no comparisons have been made for both clear and cloudy skies across multiple sites. This study evaluates the accuracy of 13 algorithms for predicting incident long-wave radiation under clear skies, ten cloud correction algorithms, and four algorithms for all-sky conditions using data from 21 sites across North America and China. Data from five research sites were combined with publicly available data from nine sites in the AmeriFlux network for initial evaluation and optimization of cloud cover estimates; seven additional AmeriFlux sites were used as an independent test of the algorithms. Clear-sky algorithms that excelled in predicting Lclr were the Dilley, Prata, and Angström algorithms. Root mean square deviation (RMSD) between predicted and measured 30-minute or hourly Lclr averaged approximately 23 W m-2 for these three algorithms across all sites, while RMSD of daily estimates was as low as 14 W m-2. Cloud-correction algorithms of Kimball, Unsworth, and Crawford described the data best when combined with the Dilley clear-sky algorithm. Average RMSD across all sites for these three cloud corrections was approximately 24 to 25 W m -2 for 30-minute or hourly estimates and approximately 15 to 16 W m-2 for daily estimates. The Kimball and Unsworth cloud corrections require an estimate of cloud cover, while the Crawford algorithm corrects for cloud cover directly from measured solar radiation. Optimum limits in the clearness index, defined as the ratio of observed solar radiation to theoretical terrestrial solar radiation, for complete cloud cover and clear skies were suggested for the Kimball and Unsworth algorithms. Application of the optimized algorithms to seven independent sites yielded similar results. On the basis of the results, the recommended algorithms can be applied with reasonable accuracy for a wide range of climates, elevations, and latitudes. © 2009 by American Geophysical Union

Large-scale 13C-flux analysis reveals distinct transcriptional control of respiratory and fermentative metabolism in Escherichia coli

The authors analyze the role transcription plays in regulating bacterial metabolic flux. Of 91 transcriptional regulators studied, 2/3 affect absolute fluxes, but only a small number of regulators control the partitioning of flux between different metabolic pathways

Evolution of Soil Carbon Storage and Morphometric Properties of Afforested Soils in the U.S. Great Plains

The objective of this project was to use detailed soil profi le descriptions and soil carbon analyses to determine the soil C sequestration potential of tree planting across climatic gradients in the U.S. Great Plains. Tree windbreak age ranged from 19 to 70 years and age of cultivation from 22 to ~110 years. At each site, soil pits were prepared within the tree planting, the adjacent crop fi elds, and nearby undisturbed grassland. Windbreak soils had consistently thicker soil organic carbon (SOC)- enriched A or A+AB horizons when compared to the crop fi elds. The thickness of A or A+AB horizons in the windbreak soils were comparable to the undisturbed grassland soils. A linear relationship was detected between the difference in A+AB thickness of soils beneath windbreaks and undisturbed grasslands and a climate index (hydrothermal coeffi cient, HTC). These results indicate that tree windbreaks with more cool and moist climate conditions are more favorable for SOC accumulation in the surface soil. The relationship between SOC accumulation and climate factors enables the estimation of soil carbon stocks in existing windbreaks and the prediction of potential carbon sequestration of future plantings

Structural and functional characterization of Pseudomonas aeruginosa CupB chaperones

Pseudomonas aeruginosa, an important human pathogen, is estimated to be responsible for,10% of nosocomial infections worldwide. The pathogenesis of P. aeruginosa starts from its colonization in the damaged tissue or medical devices (e. g. catheters, prothesis and implanted heart valve etc.) facilitated by several extracellular adhesive factors including fimbrial pili. Several clusters containing fimbrial genes have been previously identified on the P. aeruginosa chromosome and named cup [1]. The assembly of the CupB pili is thought to be coordinated by two chaperones, CupB2 and CupB4. However, due to the lack of structural and biochemical data, their chaperone activities remain speculative. In this study, we report the 2.5 A crystal structure of P. aeruginosa CupB2. Based on the structure, we further tested the binding specificity of CupB2 and CupB4 towards CupB1 (the presumed major pilus subunit) and CupB6 (the putative adhesin) using limited trypsin digestion and strep-tactin pull-down assay. The structural and biochemical data suggest that CupB2 and CupB4 might play different, but not redundant, roles in CupB secretion. CupB2 is likely to be the chaperone of CupB1, and CupB4 could be the chaperone of CupB4:CupB5:CupB6, in which the interaction of CupB4 and CupB6 might be mediated via CupB5

Guidelines for the deployment and implementation of manufacturing scheduling systems

It has frequently been stated that there exists a gap between production scheduling theory and practice. In order to put theoretical findings into practice, advances in scheduling models and solution procedures should be embedded into a piece of software - a scheduling system - in companies. This results in a process that entails (1) determining its functional features, and (2) adopting a successful strategy for its development and deployment. In this paper we address the latter question and review the related literature in order to identify descriptions and recommendations of the main aspects to be taken into account when developing such systems. These issues are then discussed and classified, resulting in a set of guidelines that can help practitioners during the process of developing and deploying a scheduling system. In addition, identification of these issues can provide some insights to drive theoretical scheduling research towards those topics more in demand by practitioners, and thus help to close the aforementioned gap.Framiñan Torres, JM.; Ruiz García, R. (2012). Guidelines for the deployment and implementation of manufacturing scheduling systems. International Journal of Production Research. 50(7):1799-1812. doi:10.1080/00207543.2011.564670S17991812507Baek, D. H. (1999). A visualized human-computer interactive approach to job shop scheduling. International Journal of Computer Integrated Manufacturing, 12(1), 75-83. doi:10.1080/095119299130489Comesaña Benavides, J. A., & Carlos Prado, J. (2002). Creating an expert system for detailed scheduling. International Journal of Operations & Production Management, 22(7), 806-819. doi:10.1108/01443570210433562Bensana, E. 1986. An expert-system approach to industrial job-shop scheduling. 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European Journal of Operational Research, 39(1), 17-31. doi:10.1016/0377-2217(89)90349-4Chen, J.-F. (2004). Unrelated parallel machine scheduling with secondary resource constraints. The International Journal of Advanced Manufacturing Technology, 26(3), 285-292. doi:10.1007/s00170-003-1622-1Collinot, A., Le Pape, C., & Pinoteau, G. (1988). SONIA: A knowledge-based scheduling system. Artificial Intelligence in Engineering, 3(2), 86-94. doi:10.1016/0954-1810(88)90024-6Cowling, P. (2003). A flexible decision support system for steel hot rolling mill scheduling. Computers & Industrial Engineering, 45(2), 307-321. doi:10.1016/s0360-8352(03)00038-xDudek, R. A., Panwalkar, S. S., & Smith, M. L. (1992). The Lessons of Flowshop Scheduling Research. Operations Research, 40(1), 7-13. doi:10.1287/opre.40.1.7Dumond, E. J. (2005). Understanding and using the capabilities of finite scheduling. Industrial Management & Data Systems, 105(4), 506-526. doi:10.1108/02635570510592398Fox, M. S., & Smith, S. F. (1984). ISIS?a knowledge-based system for factory scheduling. Expert Systems, 1(1), 25-49. doi:10.1111/j.1468-0394.1984.tb00424.xFraminan, J. M., & Ruiz, R. (2010). Architecture of manufacturing scheduling systems: Literature review and an integrated proposal. European Journal of Operational Research, 205(2), 237-246. doi:10.1016/j.ejor.2009.09.026Freed, T., Doerr, K. H., & Chang, T. (2007). In-house development of scheduling decision support systems: case study for scheduling semiconductor device test operations. International Journal of Production Research, 45(21), 5075-5093. doi:10.1080/00207540600818351Gao, C and Tang, L. 2008. A decision support system for color-coating line in steel industry. In: Proceedings of the IEEE international conference on automation and logistics, ICAL 2008. 2008. pp.1463–1468.Grant, T. J. (1986). Lessons for O.R. from A.I.: A Scheduling Case Study. Journal of the Operational Research Society, 37(1), 41-57. doi:10.1057/jors.1986.7Graves, S. C. 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Journal of Systems and Software, 19(2), 123-130. doi:10.1016/0164-1212(92)90063-pKnolmayer, G., Mertens, P., & Zeier, A. (2002). Supply Chain Management Based on SAP Systems. doi:10.1007/978-3-540-24816-3Leachman, R. C., Benson, R. F., Liu, C., & Raar, D. J. (1996). IMPReSS: An Automated Production-Planning and Delivery-Quotation System at Harris Corporation—Semiconductor Sector. Interfaces, 26(1), 6-37. doi:10.1287/inte.26.1.6MACCARTHY, B. L., & LIU, J. (1993). Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. International Journal of Production Research, 31(1), 59-79. doi:10.1080/00207549308956713McKay, K. N., & Black, G. W. (2007). The evolution of a production planning system: A 10-year case study. Computers in Industry, 58(8-9), 756-771. doi:10.1016/j.compind.2007.02.002McKay, K. N., Safayeni, F. R., & Buzacott, J. A. (1988). Job-Shop Scheduling Theory: What Is Relevant? Interfaces, 18(4), 84-90. doi:10.1287/inte.18.4.84McKay, K. N., Morton, T. E., Ramnath, P., & Wang, J. (2000). ?Aversion dynamics? scheduling when the system changes. Journal of Scheduling, 3(2), 71-88. doi:10.1002/(sici)1099-1425(200003/04)3:23.0.co;2-0MCKAY, K., PINEDO, M., & WEBSTER, S. (2009). PRACTICE-FOCUSED RESEARCH ISSUES FOR SCHEDULING SYSTEMS*. Production and Operations Management, 11(2), 249-258. doi:10.1111/j.1937-5956.2002.tb00494.xMissbauer, H., Hauber, W., & Stadler, W. (2009). A scheduling system for the steelmaking-continuous casting process. A case study from the steel-making industry. International Journal of Production Research, 47(15), 4147-4172. doi:10.1080/00207540801950136Numao, M and Morishita, S. 1989. A scheduling environment for steel-making processes. In: Proceedings of the 5th conference on artificial intelligence applications. 1989. pp.279–286.Olhager, J., & Rapp, B. (1995). Operations Research Techniques in Manufacturing Planning and Control Systems. International Transactions in Operational Research, 2(1), 29-43. doi:10.1111/j.1475-3995.1995.tb00003.xPerez-Gonzalez, P., & Framinan, J. M. (2009). Scheduling permutation flowshops with initial availability constraint: Analysis of solutions and constructive heuristics. Computers & Operations Research, 36(10), 2866-2876. doi:10.1016/j.cor.2008.12.018Pinedo, M., & Yen, B. P.-C. (1997). Annals of Operations Research, 70, 359-378. doi:10.1023/a:1018986524234Portougal, V., & Robb, D. J. (2000). Production Scheduling Theory: Just Where Is It Applicable? Interfaces, 30(6), 64-76. doi:10.1287/inte.30.6.64.11623Reisman, A., Kumar, A., & Motwani, J. (1997). Flowshop scheduling/sequencing research: a statistical review of the literature, 1952-1994. IEEE Transactions on Engineering Management, 44(3), 316-329. doi:10.1109/17.618173Steffen, MS. 1986. A survey of artificial intelligence-based scheduling systems. In: Proceedings of the fall industrial engineering conference. 1986.Storer, R. H., Wu, S. D., & Vaccari, R. (1992). New Search Spaces for Sequencing Problems with Application to Job Shop Scheduling. Management Science, 38(10), 1495-1509. doi:10.1287/mnsc.38.10.1495Tang, L., & Wang, G. (2008). Decision support system for the batching problems of steelmaking and continuous-casting production. Omega, 36(6), 976-991. doi:10.1016/j.omega.2007.11.002T’kindt, V., Billaut, J.-C., Bouquard, J.-L., Lenté, C., Martineau, P., Néron, E., … Tacquard, C. (2005). The e-OCEA project: towards an Internet decision system for scheduling problems. Decision Support Systems, 40(2), 329-337. doi:10.1016/j.dss.2004.04.001Wiers, VCS. 1997. Human–computer interaction in production scheduling: Analysis and design of decision support systems for production scheduling tasks. Ph.D. Thesis, Technische Universiteit Eindhoven, NetherlandsWiers, V. C. S. (2002). A case study on the integration of APS and ERP in a steel processing plant. 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An optical supernova associated with the X-ray flash XRF 060218

Long-duration gamma-ray bursts (GRBs) are associated with type Ic supernovae that are more luminous than average and that eject material at very high velocities. Less-luminous supernovae were not hitherto known to be associated with GRBs, and therefore GRB-supernovae were thought to be rare events. Whether X-ray flashes - analogues of GRBs, but with lower luminosities and fewer gamma-rays - can also be associated with supernovae, and whether they are intrinsically 'weak' events or typical GRBs viewed off the axis of the burst, is unclear. Here we report the optical discovery and follow-up observations of the type Ic supernova SN 2006aj associated with X-ray flash XRF 060218. Supernova 2006aj is intrinsically less luminous than the GRB-supernovae, but more luminous than many supernovae not accompanied by a GRB. The ejecta velocities derived from our spectra are intermediate between these two groups, which is consistent with the weakness of both the GRB output and the supernova radio flux. Our data, combined with radio and X-ray observations, suggest that XRF 060218 is an intrinsically weak and soft event, rather than a classical GRB observed off-axis. This extends the GRB-supernova connection to X-ray flashes and fainter supernovae, implying a common origin. Events such as XRF 060218 are probably more numerous than GRB-supernovae.Comment: Final published versio

Role of lipid-mobilising factor (LMF) in protecting tumour cells from oxidative damage

Lipid-mobilising factor (LMF) is produced by cachexia-inducing tumours and is involved in the degradation of adipose tissue, with increased oxidation of the released fatty acids through an induction of uncoupling protein (UCP) expression. Since UCP-2 is thought to be involved in the detoxification of free radicals if LMF induced UCP-2 expression in tumour cells, it might attenuate free radical toxicity. As a model system we have used MAC13 tumour cells, which do not produce LMF. Addition of LMF caused a concentration-dependent increase in UCP-2 expression, as determined by immunoblotting. This effect was attenuated by the β3 antagonist SR59230A, suggesting that it was mediated through a β3 adrenoreceptor. Co-incubation of LMF with MAC13 cells reduced the growth-inhibitory effects of bleomycin, paraquat and hydrogen peroxide, known to be free radical generators, but not chlorambucil, an alkylating agent. There was no effect of LMF alone on cellular proliferation. These results indicate that LMF antagonises the antiproliferative effect of agents working through a free radical mechanism, and may partly explain the unresponsiveness to the chemotherapy of cachexia-inducing tumours. © 2004 Cancer Research UK

Definitive radiotherapy and Single-Agent radiosensitizing Ifosfamide in Patients with localized, irresectable Soft Tissue Sarcoma: A retrospective analysis

<p>Abstract</p> <p>Background and Purpose</p> <p>Standard therapy for soft-tissue sarcomas remains complete resection. For primary radiotherapy local control rates of 30-45% have been reported. We analyzed retrospectively 11 cases of radiochemotherapy with single-agent ifosfamide in patients with macroscopic soft-tissue sarcomas.</p> <p>Patients and Methods</p> <p>The patients were treated in irresectable high risk situations. Radiation therapy was performed with median 60 Gy. During the first and fifth week the concomitant chemotherapy with ifosfamide was added. Two patients received trimodal therapy with additional regional hyperthermia.</p> <p>Results</p> <p>The therapy was completed in 73% of the patients. Average local control time was 91 months, median disease-free-survival/overall-survival was 8/26 months. Five-year rates for local control/disease free survival/overall survival were 70%/34%/34%. The limited prognosis is mainly caused by systemic treatment failure.</p> <p>Conclusions</p> <p>The data strongly suggest a better outcome of radiochemotherapy with ifosfamide compared to radiotherapy alone and radiotherapy in combination with other radiosensitizers.</p

Biosynthesis of Vitamin C by Yeast Leads to Increased Stress Resistance

during respiration, or indirectly-caused by other stressing factors. Vitamin C or L-ascorbic acid acts as a scavenger of ROS, thereby potentially protecting cells from harmful oxidative products. While most eukaryotes synthesize ascorbic acid, yeast cells produce erythro-ascorbic acid instead. The actual importance of this antioxidant substance for the yeast is still a subject of scientific debate. is increased, but also the tolerance to low pH and weak organic acids at low pH is increased. cells endogenously producing vitamin C as a cellular model to study the genesis/protection of ROS as well as genotoxicity

Consumer perceptions of co-branding alliances: Organizational dissimilarity signals and brand fit

This study explores how consumers evaluate co-branding alliances between dissimilar partner firms. Customers are well aware that different firms are behind a co-branded product and observe the partner firms’ characteristics. Drawing on signaling theory, we assert that consumers use organizational characteristics as signals in their assessment of brand fit and for their purchasing decisions. Some organizational signals are beyond the control of the co-branding partners or at least they cannot alter them on short notice. We use a quasi-experimental design and test how co-branding partner dissimilarity affects brand fit perception. The results show that co-branding partner dissimilarity in terms of firm size, industry scope, and country-of-origin image negatively affects brand fit perception. Firm age dissimilarity does not exert significant influence. Because brand fit generally fosters a benevolent consumer attitude towards a co-branding alliance, the findings suggest that high partner dissimilarity may reduce overall co-branding alliance performance