Using Quality Literature with At-Risk Secondary School Student

This article has two purposes: to present a rationale for using quality literature with at-risk students and to present effective strategies with which to do so. Instruction for at-risk students is often inappropriate for two major reasons. First, at-risk students are often mistakenly assumed to be students with low abilities and low levels of experience. Second, the educational goals for at-risk students are often inappropriately low

Assessment of Ice Shape Roughness Using a Self-Orgainizing Map Approach

Self-organizing maps are neural-network techniques for representing noisy, multidimensional data aligned along a lower-dimensional and nonlinear manifold. For a large set of noisy data, each element of a finite set of codebook vectors is iteratively moved in the direction of the data closest to the winner codebook vector. Through successive iterations, the codebook vectors begin to align with the trends of the higher-dimensional data. Prior investigations of ice shapes have focused on using self-organizing maps to characterize mean ice forms. The Icing Research Branch has recently acquired a high resolution three dimensional scanner system capable of resolving ice shape surface roughness. A method is presented for the evaluation of surface roughness variations using high-resolution surface scans based on a self-organizing map representation of the mean ice shape. The new method is demonstrated for 1) an 18-in. NACA 23012 airfoil 2 AOA just after the initial ice coverage of the leading 5 of the suction surface of the airfoil, 2) a 21-in. NACA 0012 at 0AOA following coverage of the leading 10 of the airfoil surface, and 3) a cold-soaked 21-in.NACA 0012 airfoil without ice. The SOM method resulted in descriptions of the statistical coverage limits and a quantitative representation of early stages of ice roughness formation on the airfoils. Limitations of the SOM method are explored, and the uncertainty limits of the method are investigated using the non-iced NACA 0012 airfoil measurements

Microhabitat preferences of fish assemblages in the Udzungwa Mountains (Eastern Africa)

[EN] Environmental flow assessment (EFA) involving microhabitat preference models is a common approach to set ecologically friendly flow regimes in territories with ongoing or planned projects to develop river basins, such as many rivers of Eastern Africa. However, habitat requirements of many African fish species are poorly studied, which may impair EFAs. This study investigated habitat preferences of fish assemblages, based on species presence-absence data from 300 microhabitats collected in two tributaries of the Kilombero River (Tanzania), aiming to disentangle differences in habitat preferences of African species at two levels: assemblage (i.e. between tributaries) and species (i.e. species-specific habitat preferences). Overall, flow velocity, which implies coarser substrates and shallower microhabitats, emerged as the most important driver responsible of the changes in stream-dwelling assemblages at the microhabitat scale. At the assemblage level, we identified two important groups of species according to habitat preferences: (a) cover-orientated and limnophilic species, including Barbus spp., Mormyridae and Chiloglanis deckenii, and (b) rheophilic species, including Labeo cylindricus, Amphilius uranoscopus and Parakneria spekii. Rheophilic species preferred boulders, fast flow velocity and deeper microhabitats. At the species level, we identified species-specific habitat preferences. For instance, Barbus spp. preferred low flow velocity shallow depth and fine-to-medium substratum, whereas L. cylindricus and P. spekii mainly selected shallow microhabitats with coarse substrata. Knowledge of habitat preferences of these assemblages and species should enhance the implementation of ongoing and future EFA studies of the region.We thank C. Alexander and an anonymous referee for constructive comments on the submitted manuscript. This study was financed by the United States Agency for International Development (USAID) as part of the Technical Assistance to Support the Development of Irrigation and Rural Roads Infrastructure Project (IRRIP2), implemented by CDM International Inc. We are particularly grateful to the local people who helped us during the data collection. We also gratefully acknowledge individuals from organisations that collaborated in this research and especially the scientific committee that shared their knowledge of the Kilombero River basin. These individuals include the following: J.J. Kashaigili (SUA), K.N. Njau (NM. AIST), P.M. Ndomba (UDSM), F. Mombo (SUA), S. Graas (UNESCO- IHE), C.M. Mengo (RUFIJI BASIN), J.H. O'keeffe (Rhodes Univ.), S.M. Andrew (SUA), P. Paron (UNESCO-IHE), W. Kasanga (CDM Smith), and R. Tharme (RIVER FUTURES). R. Muñoz-Mas benefitted from a postdoctoral Juan de la Cierva fellowship from the Spanish Ministry of Science, Innovation and Universities (ref. FJCI-2016-30829) and J. Sánchez-Hernández was supported by a postdoctoral grant from the Galician Plan for Research, Innovation and Growth (Plan I2C, Xunta de Galicia). Additional funding was provided by the Ministry of Science, Innovation and Universities (projects CGL2016-80820-R and PCIN-2016-168) and the Government of Catalonia (ref. 2017 SGR 548).Muñoz-Mas, R.; Sánchez-Hernández, J.; Martinez-Capel, F.; Tamatamah, R.; Mohamedi, S.; Massinde, R.; Mcclain, ME. (2019). Microhabitat preferences of fish assemblages in the Udzungwa Mountains (Eastern Africa). Ecology Of Freshwater Fish. 28(3):473-484. https://doi.org/10.1111/eff.12469S473484283Akbaripasand, A., & Closs, G. P. (2017). Effects of food supply and stream physical characteristics on habitat use of a stream-dwelling fish. Ecology of Freshwater Fish, 27(1), 270-279. doi:10.1111/eff.12345Alexander, C., Poulsen, F., Robinson, D. C. E., Ma, B. O., … Luster, R. A. (2018). Improving Multi-Objective Ecological Flow Management with Flexible Priorities and Turn-Taking: A Case Study from the Sacramento River and Sacramento–San Joaquin Delta. San Francisco Estuary and Watershed Science, 16(1). doi:10.15447/sfews.2018v16iss1/art2ALLOUCHE, S. (2002). NATURE AND FUNCTIONS OF COVER FOR RIVERINE FISH. Bulletin Français de la Pêche et de la Pisciculture, (365-366), 297-324. doi:10.1051/kmae:2002037Ardia, D., Boudt, K., Carl, P., Mullen, K., M., & Peterson, B., G. (2011). Differential Evolution with DEoptim. The R Journal, 3(1), 27. doi:10.32614/rj-2011-005Arthington, A. H., Bunn, S. E., Poff, N. L., & Naiman, R. J. (2006). THE CHALLENGE OF PROVIDING ENVIRONMENTAL FLOW RULES TO SUSTAIN RIVER ECOSYSTEMS. Ecological Applications, 16(4), 1311-1318. doi:10.1890/1051-0761(2006)016[1311:tcopef]2.0.co;2Austin, M. (2007). Species distribution models and ecological theory: A critical assessment and some possible new approaches. Ecological Modelling, 200(1-2), 1-19. doi:10.1016/j.ecolmodel.2006.07.005Bain, M. B., Finn, J. T., & Booke, H. E. (1985). A Quantitative Method for Sampling Riverine Microhabitats by Electrofishing. North American Journal of Fisheries Management, 5(3B), 489-493. doi:10.1577/1548-8659(1985)52.0.co;2Baselga, A., & Araújo, M. B. (2009). Individualistic vs community modelling of species distributions under climate change. Ecography, 32(1), 55-65. doi:10.1111/j.1600-0587.2009.05856.xCAMP, E. V., GWINN, D. C., PINE III, W. E., & FRAZER, T. K. (2011). Changes in submersed aquatic vegetation affect predation risk of a common prey fish Lucania parva (Cyprinodontiformes: Fundulidae) in a spring-fed coastal river. Fisheries Management and Ecology, 19(3), 245-251. doi:10.1111/j.1365-2400.2011.00827.xCheng, B., & Li, H. (2018). Agricultural economic losses caused by protection of the ecological basic flow of rivers. Journal of Hydrology, 564, 68-75. doi:10.1016/j.jhydrol.2018.06.065Cotula, L. (2012). The international political economy of the global land rush: A critical appraisal of trends, scale, geography and drivers. The Journal of Peasant Studies, 39(3-4), 649-680. doi:10.1080/03066150.2012.674940Dudgeon, D. (2000). The Ecology of Tropical Asian Rivers and Streams in Relation to Biodiversity Conservation. Annual Review of Ecology and Systematics, 31(1), 239-263. doi:10.1146/annurev.ecolsys.31.1.239Eccles D. H.(1992).Field guide to the freshwater fishes of Tanzania. FAO species identification sheets for fishery purposes.Rome Italy:FAO: Food & Agriculture Organization of the United Nations.Elisa, M., Gara, J. I., & Wolanski, E. (2010). A review of the water crisis in Tanzania’s protected areas, with emphasis on the Katuma River—Lake Rukwa ecosystem. Ecohydrology & Hydrobiology, 10(2-4), 153-165. doi:10.2478/v10104-011-0001-zFriedman, J. H. (2001). machine. The Annals of Statistics, 29(5), 1189-1232. doi:10.1214/aos/1013203451Fukuda, S., De Baets, B., Waegeman, W., Verwaeren, J., & Mouton, A. M. (2013). Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models. Environmental Modelling & Software, 47, 1-6. doi:10.1016/j.envsoft.2013.04.005Fukuda, S., Mouton, A. M., & De Baets, B. (2011). Abundance versus presence/absence data for modelling fish habitat preference with a genetic Takagi–Sugeno fuzzy system. Environmental Monitoring and Assessment, 184(10), 6159-6171. doi:10.1007/s10661-011-2410-2Garbe, J., Beevers, L., & Pender, G. (2016). The interaction of low flow conditions and spawning brown trout ( Salmo trutta ) habitat availability. Ecological Engineering, 88, 53-63. doi:10.1016/j.ecoleng.2015.12.011Gibson, R. J. (1993). The Atlantic salmon in fresh water: spawning, rearing and production. Reviews in Fish Biology and Fisheries, 3(1), 39-73. doi:10.1007/bf00043297Ibanez, C., Oberdorff, T., Teugels, G., Mamononekene, V., Lavoué, S., Fermon, Y., … Toham, A. K. (2007). Fish assemblages structure and function along environmental gradients in rivers of Gabon (Africa). Ecology of Freshwater Fish, 16(3), 315-334. doi:10.1111/j.1600-0633.2006.00222.xJOHNSON, J. H., & DOUGLASS, K. A. (2009). Diurnal stream habitat use of juvenile Atlantic salmon, brown trout and rainbow trout in winter. Fisheries Management and Ecology, 16(5), 352-359. doi:10.1111/j.1365-2400.2009.00680.xKadye, W. T., & Chakona, A. (2012). Spatial and temporal variation of fish assemblage in two intermittent streams in north-western Zimbabwe. African Journal of Ecology, 50(4), 428-438. doi:10.1111/j.1365-2028.2012.01338.xKadye, W. T., & Moyo, N. A. G. (2008). Stream fish assemblage and habitat structure in a tropical African river basin (Nyagui River, Zimbabwe). African Journal of Ecology, 46(3), 333-340. doi:10.1111/j.1365-2028.2007.00843.xKouamé, K. A., Yao, S. S., Gooré Bi, G., Kouamélan, E. P., N’Douba, V., & Kouassi, N. J. (2007). Influential environmental gradients and patterns of fish assemblages in a West African basin. Hydrobiologia, 603(1), 159-169. doi:10.1007/s10750-007-9256-1Logez, M., Bady, P., & Pont, D. (2011). Modelling the habitat requirement of riverine fish species at the European scale: sensitivity to temperature and precipitation and associated uncertainty. Ecology of Freshwater Fish, 21(2), 266-282. doi:10.1111/j.1600-0633.2011.00545.xMaguire, K. C., Nieto-Lugilde, D., Blois, J. L., Fitzpatrick, M. C., Williams, J. W., Ferrier, S., & Lorenz, D. J. (2016). Controlled comparison of species- and community-level models across novel climates and communities. Proceedings of the Royal Society B: Biological Sciences, 283(1826), 20152817. doi:10.1098/rspb.2015.2817McClain, M. E., Kashaigili, J. J., & Ndomba, P. (2013). Environmental flow assessment as a tool for achieving environmental objectives of African water policy, with examples from East Africa. International Journal of Water Resources Development, 29(4), 650-665. doi:10.1080/07900627.2013.781913McClain, M. E., Subalusky, A. L., Anderson, E. P., Dessu, S. B., Melesse, A. M., Ndomba, P. M., … Mligo, C. (2014). Comparing flow regime, channel hydraulics, and biological communities to infer flow–ecology relationships in the Mara River of Kenya and Tanzania. Hydrological Sciences Journal, 59(3-4), 801-819. doi:10.1080/02626667.2013.853121Mouton, A. M., Alcaraz-Hernández, J. D., De Baets, B., Goethals, P. L. M., & Martínez-Capel, F. (2011). Data-driven fuzzy habitat suitability models for brown trout in Spanish Mediterranean rivers. Environmental Modelling & Software, 26(5), 615-622. doi:10.1016/j.envsoft.2010.12.001Mouton, A. M., De Baets, B., & Goethals, P. L. M. (2010). Ecological relevance of performance criteria for species distribution models. Ecological Modelling, 221(16), 1995-2002. doi:10.1016/j.ecolmodel.2010.04.017Mouton, A. M., Schneider, M., Peter, A., Holzer, G., Müller, R., Goethals, P. L. M., & De Pauw, N. (2008). Optimisation of a fuzzy physical habitat model for spawning European grayling (Thymallus thymallus L.) in the Aare river (Thun, Switzerland). Ecological Modelling, 215(1-3), 122-132. doi:10.1016/j.ecolmodel.2008.02.028Mullen, K., Ardia, D., Gil, D., Windover, D., & Cline, J. (2011). DEoptim: AnRPackage for Global Optimization by Differential Evolution. Journal of Statistical Software, 40(6). doi:10.18637/jss.v040.i06Muñoz-Mas, R., Marcos-Garcia, P., Lopez-Nicolas, A., Martínez-García, F. J., Pulido-Velazquez, M., & Martínez-Capel, F. (2018). Combining literature-based and data-driven fuzzy models to predict brown trout (Salmo trutta L.) spawning habitat degradation induced by climate change. Ecological Modelling, 386, 98-114. doi:10.1016/j.ecolmodel.2018.08.012Muñoz-Mas, R., Martínez-Capel, F., Alcaraz-Hernández, J. D., & Mouton, A. M. (2015). Can multilayer perceptron ensembles model the ecological niche of freshwater fish species? Ecological Modelling, 309-310, 72-81. doi:10.1016/j.ecolmodel.2015.04.025Muñoz-Mas, R., Martínez-Capel, F., Alcaraz-Hernández, J. D., & Mouton, A. M. (2017). On species distribution modelling, spatial scales and environmental flow assessment with Multi–Layer Perceptron Ensembles: A case study on the redfin barbel (Barbus haasi; Mertens, 1925). Limnologica, 62, 161-172. doi:10.1016/j.limno.2016.09.004Muñoz-Mas, R., Martínez-Capel, F., Schneider, M., & Mouton, A. M. (2012). Assessment of brown trout habitat suitability in the Jucar River Basin (SPAIN): Comparison of data-driven approaches with fuzzy-logic models and univariate suitability curves. Science of The Total Environment, 440, 123-131. doi:10.1016/j.scitotenv.2012.07.074Muñoz-Mas, R., Papadaki, C., Martínez-Capel, F., Zogaris, S., Ntoanidis, L., & Dimitriou, E. (2016). Generalized additive and fuzzy models in environmental flow assessment: A comparison employing the West Balkan trout (Salmo farioides; Karaman, 1938). Ecological Engineering, 91, 365-377. doi:10.1016/j.ecoleng.2016.03.009Ngugi, C. C., Manyala, J. O., Njiru, M., & Mlewa, C. M. (2009). Some aspects of the biology of the stargazer mountain catfish,Amphilius uranoscopus(pfeffer); (Siluriformes: Amphiliidae) indigenous to Kenya streams. African Journal of Ecology, 47(4), 606-613. doi:10.1111/j.1365-2028.2009.01032.xNovák, V., & Lehmke, S. (2006). Logical structure of fuzzy IF-THEN rules. Fuzzy Sets and Systems, 157(15), 2003-2029. doi:10.1016/j.fss.2006.02.011Pease, A. A., Taylor, J. M., Winemiller, K. O., & King, R. S. (2015). Ecoregional, catchment, and reach-scale environmental factors shape functional-trait structure of stream fish assemblages. Hydrobiologia, 753(1), 265-283. doi:10.1007/s10750-015-2235-zPetts, G. E. (2009). Instream Flow Science For Sustainable River Management. JAWRA Journal of the American Water Resources Association, 45(5), 1071-1086. doi:10.1111/j.1752-1688.2009.00360.xPOFF, N. L., & ZIMMERMAN, J. K. H. (2010). Ecological responses to altered flow regimes: a literature review to inform the science and management of environmental flows. Freshwater Biology, 55(1), 194-205. doi:10.1111/j.1365-2427.2009.02272.xPoff, N. L., Allan, J. D., Bain, M. B., Karr, J. R., Prestegaard, K. L., Richter, B. D., … Stromberg, J. C. (1997). The Natural Flow Regime. BioScience, 47(11), 769-784. doi:10.2307/1313099POFF, N. L., RICHTER, B. D., ARTHINGTON, A. H., BUNN, S. E., NAIMAN, R. J., KENDY, E., … WARNER, A. (2010). The ecological limits of hydrologic alteration (ELOHA): a new framework for developing regional environmental flow standards. Freshwater Biology, 55(1), 147-170. doi:10.1111/j.1365-2427.2009.02204.xReiser, D. W., & Hilgert, P. J. (2018). A Practitioner’s Perspective on the Continuing Technical Merits of PHABSIM. Fisheries, 43(6), 278-283. doi:10.1002/fsh.10082ROBERTS, T. R. (1975). Geographical distribution of African freshwater fishes. Zoological Journal of the Linnean Society, 57(4), 249-319. doi:10.1111/j.1096-3642.1975.tb01893.xSánchez-Hernández, J., Gabler, H.-M., & Amundsen, P.-A. (2017). Prey diversity as a driver of resource partitioning between river-dwelling fish species. Ecology and Evolution, 7(7), 2058-2068. doi:10.1002/ece3.2793Scheidegger, K. J., & Bain, M. B. (1995). Larval Fish Distribution and Microhabitat Use in Free-Flowing and Regulated Rivers. Copeia, 1995(1), 125. doi:10.2307/1446807SCHMIDT, R. C., BART, H. L. J., & NYINGI, W. D. (2015). Two new species of African suckermouth catfishes, genus Chiloglanis (Siluriformes: Mochokidae), from Kenya with remarks on other taxa from the area. Zootaxa, 4044(1), 45. doi:10.11646/zootaxa.4044.1.2Schoelynck, J., Creëlle, S., Buis, K., De Mulder, T., Emsens, W.-J., Hein, T., … Folkard, A. (2018). What is a macrophyte patch? Patch identification in aquatic ecosystems and guidelines for consistent delineation. Ecohydrology & Hydrobiology, 18(1), 1-9. doi:10.1016/j.ecohyd.2017.10.005Skelton P. H.(2001).A complete guide to the freshwater fishes of southern Africa. Struik.Somodi, I., Lepesi, N., & Botta-Dukát, Z. (2017). Prevalence dependence in model goodness measures with special emphasis on true skill statistics. Ecology and Evolution, 7(3), 863-872. doi:10.1002/ece3.2654Storn, R., & Price, K. (1997). Journal of Global Optimization, 11(4), 341-359. doi:10.1023/a:1008202821328Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(1), 116-132. doi:10.1109/tsmc.1985.6313399Tharme, R. E. (2003). A global perspective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. River Research and Applications, 19(5-6), 397-441. doi:10.1002/rra.736Theodoropoulos, C., Skoulikidis, N., Stamou, A., & Dimitriou, E. (2018). Spatiotemporal Variation in Benthic-Invertebrates-Based Physical Habitat Modelling: Can We Use Generic Instead of Local and Season-Specific Habitat Suitability Criteria? Water, 10(11), 1508. doi:10.3390/w10111508Vadas, R. L., Vadas, R. L., & Orth, D. J. (2000). Environmental Biology of Fishes, 59(3), 253-269. doi:10.1023/a:1007613701843Van Oosterhout, M. P., van der Velde, G., & Gaigher, I. G. (2008). High altitude mountain streams as a possible refuge habitat for the catfish Amphilius uranoscopus. Environmental Biology of Fishes, 84(1), 109-120. doi:10.1007/s10641-008-9394-yVezza, P., Parasiewicz, P., Rosso, M., & Comoglio, C. (2011). DEFINING MINIMUM ENVIRONMENTAL FLOWS AT REGIONAL SCALE: APPLICATION OF MESOSCALE HABITAT MODELS AND CATCHMENTS CLASSIFICATION. River Research and Applications, 28(6), 717-730. doi:10.1002/rra.1571Vilizzi, L., Stakenas, S., & Copp, G. H. (2012). Use of constrained additive and quadratic ordination in fish habitat studies: an application to introduced pumpkinseed (Lepomis gibbosus) and native brown trout (Salmo trutta) in an English stream. Fundamental and Applied Limnology, 180(1), 69-75. doi:10.1127/1863-9135/2012/0277Webb, J. A., de Little, S. C., Miller, K. A., & Stewardson, M. J. (2018). Quantifying and predicting the benefits of environmental flows: Combining large-scale monitoring data and expert knowledge within hierarchical Bayesian models. Freshwater Biology, 63(8), 831-843. doi:10.1111/fwb.13069Wisz, M. S., Hijmans, R. J., Li, J., Peterson, A. T., Graham, C. H., & Guisan, A. (2008). Effects of sample size on the performance of species distribution models. Diversity and Distributions, 14(5), 763-773. doi:10.1111/j.1472-4642.2008.00482.xWorthington E. B.(1929).A Report on the Fishing Survey of Lakes Albert and Kioga: March to July 1928. Government of Uganda Protectorate by the Crown Agents for the Colonies.Yee, T. W. (2006). CONSTRAINED ADDITIVE ORDINATION. Ecology, 87(1), 203-213. doi:10.1890/05-0283Yee, T. W. (2010). TheVGAMPackage for Categorical Data Analysis. Journal of Statistical Software, 32(10). doi:10.18637/jss.v032.i10Yen, J., & Liang Wang. (1998). Application of statistical information criteria for optimal fuzzy model construction. IEEE Transactions on Fuzzy Systems, 6(3), 362-372. doi:10.1109/91.705503Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xZhou, S.-M., & Gan, J. Q. (2008). Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling. Fuzzy Sets and Systems, 159(23), 3091-3131. doi:10.1016/j.fss.2008.05.01

Hagedorn transition for strings on pp-waves and tori with chemical potentials

It has been conjectured that string theory in a pp-wave background is dual to a sector of N=4 supersymmetric Yang-Mills theory. We study the Hagedorn transition for free strings in this background. We find that the free energy at the transition point is finite suggesting a confinement/deconfinement transition in the gauge theory. In the limit of vanishing mass parameter the free energy matches that of free strings on an 8-torus with momentum/winding chemical potential. The entropy in the microcanonical ensemble with fixed energy and fixed momentum/winding is computed in each case.Comment: 15 pages, latex, references added and minor correction

The Ursinus Weekly, March 8, 1973

The new USGA council: a personal profile • Whitians accept thirteen new members for 1973 • International Relations Club to hold mock U.N. session • Mini-computers take Ursinus by storm • USGA implements procedures to strengthen Paisley security • Editorial: Secret war and peace • Faculty portrait: Professor G. Sieber Pancoast • Ursinus veterans compare military, academic life • Lantern plans contest, May issue • Festival of arts: Folk group presents concert in Union, then a workshop; Ballet exhibition given by Schuylkill Valley company; ProTheatre\u27s three short plays well received; Arts weekend rounded out by bazaar, mixer and madrigals; Chaplin\u27s The Circus delights Sunday evening crowd • Faculty discuss the comprehensive exams • New Union cook takes charge, does job well • Bouncing Bearettes crush E-burg; Birdie belting set smash opponents • Team evens season; Sheli Bower returns • Sports buffs corner • Bears top Eastern in season finalehttps://digitalcommons.ursinus.edu/weekly/1099/thumbnail.jp

Effect of rhPDGF-BB on bone turnover during periodontal repair

Purpose : Growth factors such as platelet-derived growth factor (PDGF) exert potent effects on wound healing including the regeneration of periodontia. Pyridinoline cross-linked carboxyterminal telopeptide of type I collagen (ICTP) is a well-known biomarker of bone turnover, and as such is a potential indicator of osseous metabolic activity. The objective of this study was to evaluate the release of the ICTP into the periodontal wound fluid (WF) following periodontal reconstructive surgery using local delivery of highly purified recombinant human PDGF (rhPDGF)-BB. Methods : Forty-seven human subjects at five treatment centres possessing chronic severe periodontal disease were monitored longitudinally for 24 weeks following PDGF regenerative surgical treatment. Severe periodontal osseous defects were divided into one of three groups and treated at the time of surgery with either: Β -tricalcium phosphate (TCP) osteoconductive scaffold alone (active control), Β -TCP+0.3 mg/ml of rhPDGF-BB, or Β -TCP+1.0 mg/ml of rhPDGF-BB. WF was harvested and analysed for local ICTP levels by radioimmunoassay. Statistical analysis was performed using analysis of variance and an area under the curve analysis (AUC). Results : The 0.3 and 1.0 mg/ml PDGF-BB treatment groups demonstrated increases in the amount of ICTP released locally for up to 6 weeks. There were statistically significant differences at the week 6 time point between Β -TCP carrier alone group versus 0.3 mg/ml PDGF-BB group ( p <0.05) and between Β -TCP alone versus the 1.0 mg/ml PDGF-BB-treated lesions ( p <0.03). The AUC analysis revealed no statistical differences amongst groups. Conclusion : This study corroborates the release of ICTP as a measure of active bone turnover following local delivery of PDGF-BB to periodontal osseous defects. The amount of ICTP released from the WF revealed an early increase for all treatment groups. Data from this study suggests that when PDGF-BB is delivered to promote periodontal tissue engineering of tooth-supporting osseous defects, there is a direct effect on ICTP released from the wound.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72239/1/j.1600-051X.2005.00870.x.pd

Two-Loop String Theory on Null Compactifications

We compute the two-loop contributions to the free energy in the null compactification of perturbative string theory at finite temperature. The cases of bosonic, Type II and heterotic strings are all treated. The calculation exploits an explicit reductive parametrization of the moduli space of infinite-momentum frame string worldsheets in terms of branched cover instantons. Various arithmetic and physical properties of the instanton sums are described. Applications to symmetric product orbifold conformal field theories and to the matrix string theory conjecture are also briefly discussed.Comment: 41 pages, 1 figur

PACE Technical Report Series, Volume 4: Cloud Retrievals in the PACE Mission: PACE Science Team Consensus Document

Earth is a complex dynamical system exhibiting continuous change in its atmosphere, ocean,and surface elements. Nearly all (99.97%) of the energy driving these systems is linked to the Sun. Measurements of reflected sunlight contain a unique signature of wavelength-specific scattering and absorption interactions occurring between incoming solar energy and atmospheric (molecules, aerosols,clouds) and surface features Clouds can affect significantly both shortwave and long wave radiation, depending on altitude/vertical structure, thermodynamic phase, and optical properties. Low, warm, and optically thick clouds predominantly have a cooling effect, while high, cold, optically thin clouds can cause warming by absorbing warmer radiation emitted from the surface and lower atmosphere.When the net difference between outgoing and incoming solar radiation is matched by the net infrared radiation emitted to space, the Earth's climate is in radiative balance. While radiative forcing components (GHGs, aerosols - direct and indirect) contribute to a net radiative imbalance, climate sensitivity is ultimately determined by the contribution of various system feed backs. The role of cloud feedback in a warming climate is currently the largest inter-model uncertainty in climate sensitivity and therefore in climate prediction [Bony and Dufresne 2005]. A comprehensive understanding of current cloud propertiesand dynamic/microphysical processes requires a global perspective from satellites

The Use of Neural Networks in Identifying Error Sources in Satellite-Derived Tropical SST Estimates

An neural network model of data mining is used to identify error sources in satellite-derived tropical sea surface temperature (SST) estimates from thermal infrared sensors onboard the Geostationary Operational Environmental Satellite (GOES). By using the Back Propagation Network (BPN) algorithm, it is found that air temperature, relative humidity, and wind speed variation are the major factors causing the errors of GOES SST products in the tropical Pacific. The accuracy of SST estimates is also improved by the model. The root mean square error (RMSE) for the daily SST estimate is reduced from 0.58 K to 0.38 K and mean absolute percentage error (MAPE) is 1.03%. For the hourly mean SST estimate, its RMSE is also reduced from 0.66 K to 0.44 K and the MAPE is 1.3%

The Geozoic Supereon

Geological time units are the lingua franca of earth sciences: they are a terminological convenience, a vernacular of any geological conversation, and a prerequisite of geo-scientific writing found throughout in earth science dictionaries and textbooks. Time units include terms formalized by stratigraphic committees as well as informal constructs erected ad hoc to communicate more efficiently. With these time terms we partition Earth’s history into utilitarian and intuitively understandable time segments that vary in length over seven orders of magnitude: from the 225-year-long Anthropocene (Crutzen and Stoermer, 2000) to the ,4-billion-year-long Precambrian (e.g., Hicks, 1885; Ball, 1906; formalized by De Villiers, 1969)