Multilevel comparison of large urban systems

For the first time the systems of cities in seven countries or regions among the largest in the world (China, India, Brazil, Europe, the Former Soviet Union (FSU), the United States and South Africa) are made comparable through the building of spatio-temporal standardised statistical databases. We first explain the concept of a generic evolutionary urban unit ("city") and its necessary adaptations to the information provided by each national statistical system. Second, the hierarchical structure and the urban growth process are compared at macro-scale for the seven countries with reference to Zipf's and Gibrat's model: in agreement with an evolutionary theory of urban systems, large similarities shape the hierarchical structure and growth processes in BRICS countries as well as in Europe and United States, despite their positions at different stages in the urban transition that explain some structural peculiarities. Third, the individual trajectories of some 10,000 cities are mapped at micro-scale following a cluster analysis of their evolution over the last fifty years. A few common principles extracted from the evolutionary theory of urban systems can explain the diversity of these trajectories, including a specific pattern in their geographical repartition in the Chinese case. We conclude that the observations at macro-level when summarized as stylised facts can help in designing simulation models of urban systems whereas the urban trajectories identified at micro-level are consistent enough for constituting the basis of plausible future population projections.Comment: 14 pages, 9 figures; Pumain, Denise, et al. "Multilevel comparison of large urban systems." Cybergeo: European Journal of Geography (2015

Accounting for climate change uncertainty in long-term dam risk management

[EN] This paper presents a practical approach to adaptive management of dam risk based on robust decision-making strategies coupled with estimation of climate scenario probabilities. The proposed methodology, called multi-prior weighted scenarios ranking, consists of a series of steps from risk estimation for current and future situations through definition of the consensus sequence of risk reduction measures to be implemented. This represents a supporting tool for dam owners and safety practitioners in making decisions for managing dams or prioritizing long-term investments using a cost-benefit approach. This methodology is applied to the case study of a Spanish dam under the effects of climate change. Several risk reduction measures are proposed and their impacts are analyzed. The application of the methodology allows for identifying the optimal sequence of implementation measures that overcomes uncertainty from the diversity of available climate scenarios by prioritizing measures that reduce future accumulated risks at lower costs. This work proves that such a methodology helps address uncertainty that arises from multiple climate scenarios while adopting a cost-benefit approach that optimizes economic resources in dam risk management.Fluixá-Sanmartín, J.; Escuder Bueno, I.; Morales-Torres, A.; Castillo-Rodríguez, J. (2021). Accounting for climate change uncertainty in long-term dam risk management. Journal of Water Resources Planning and Management. 147(4):1-13. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001355S1131474Amodio, S., D’Ambrosio, A., & Siciliano, R. (2016). Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach. European Journal of Operational Research, 249(2), 667-676. doi:10.1016/j.ejor.2015.08.048Ardiles L. D. Sanz P. Moreno E. Jenaro J. Fleitz and I. Escuder-Bueno. 2011. “Risk assessment and management for 26 Dams operated by the Duero River Authority (Spain).” In Proc. 6th Int. Conf. on Dam Engineering edited by C. Pina E. Portela and J. P. Gomes. Singapore: CI-premier Pte Ltd.Baecher, G. B., Paté, M. E., & De Neufville, R. (1980). Risk of dam failure in benefit-cost analysis. Water Resources Research, 16(3), 449-456. doi:10.1029/wr016i003p00449Burke, M., Dykema, J., Lobell, D., Miguel, E., & Satyanath, S. (2011). Incorporating Climate Uncertainty into Estimates of Climate Change Impacts, with Applications to U.S. and African Agriculture. doi:10.3386/w17092Chamberlain, G. (2000). Econometric applications of maxmin expected utility. Journal of Applied Econometrics, 15(6), 625-644. doi:10.1002/jae.583Chernet, H. H., Alfredsen, K., & Midttømme, G. H. (2014). Safety of Hydropower Dams in a Changing Climate. Journal of Hydrologic Engineering, 19(3), 569-582. doi:10.1061/(asce)he.1943-5584.0000836Choi, O. (2003). Climatic Change, 58(1/2), 149-170. doi:10.1023/a:1023459216609Christensen, J., Kjellström, E., Giorgi, F., Lenderink, G., & Rummukainen, M. (2010). Weight assignment in regional climate models. Climate Research, 44(2-3), 179-194. doi:10.3354/cr00916Danthine, J.-P., & Donaldson, J. B. (2015). Making Choices in Risky Situations. Intermediate Financial Theory, 55-86. doi:10.1016/b978-0-12-386549-6.00003-6Davis, J., Hands, D., & Mäki, U. (1998). The Handbook of Economic Methodology. doi:10.4337/9781781954249Dessai, S., & Hulme, M. (2004). Does climate adaptation policy need probabilities? Climate Policy, 4(2), 107-128. doi:10.1080/14693062.2004.9685515Eggleston H. S. 2006. “National Greenhouse Gas Inventories Programme and Chikyū Kankyō Senryaku Kenkyū Kikan.” In Proc. IPCC guidelines for national greenhouse gas inventories. Geneva: Intergovernmental Panel on Climate Change.Emond, E. J., & Mason, D. W. (2002). A new rank correlation coefficient with application to the consensus ranking problem. Journal of Multi-Criteria Decision Analysis, 11(1), 17-28. doi:10.1002/mcda.313Farnoud Hassanzadeh, F., & Milenkovic, O. (2014). An Axiomatic Approach to Constructing Distances for Rank Comparison and Aggregation. IEEE Transactions on Information Theory, 60(10), 6417-6439. doi:10.1109/tit.2014.2345760Ferson, S., & Ginzburg, L. R. (1996). Different methods are needed to propagate ignorance and variability. Reliability Engineering & System Safety, 54(2-3), 133-144. doi:10.1016/s0951-8320(96)00071-3Fluixá-Sanmartín, J., Altarejos-García, L., Morales-Torres, A., & Escuder-Bueno, I. (2018). Review article: Climate change impacts on dam safety. Natural Hazards and Earth System Sciences, 18(9), 2471-2488. doi:10.5194/nhess-18-2471-2018Fluixá-Sanmartín, J., Escuder-Bueno, I., Morales-Torres, A., & Castillo-Rodríguez, J. T. (2020). Comprehensive decision-making approach for managing time dependent dam risks. Reliability Engineering & System Safety, 203, 107100. doi:10.1016/j.ress.2020.107100Fluixá-Sanmartín, J., Morales-Torres, A., Escuder-Bueno, I., & Paredes-Arquiola, J. (2019). Quantification of climate change impact on dam failure risk under hydrological scenarios: a case study from a Spanish dam. Natural Hazards and Earth System Sciences, 19(10), 2117-2139. doi:10.5194/nhess-19-2117-2019Gersonius, B., Morselt, T., van Nieuwenhuijzen, L., Ashley, R., & Zevenbergen, C. (2012). How the Failure to Account for Flexibility in the Economic Analysis of Flood Risk and Coastal Management Strategies Can Result in Maladaptive Decisions. Journal of Waterway, Port, Coastal, and Ocean Engineering, 138(5), 386-393. doi:10.1061/(asce)ww.1943-5460.0000142Giorgi, F., & Mearns, L. O. (2002). Calculation of Average, Uncertainty Range, and Reliability of Regional Climate Changes from AOGCM Simulations via the «Reliability Ensemble Averaging» (REA) Method. Journal of Climate, 15(10), 1141-1158. doi:10.1175/1520-0442(2002)0152.0.co;2Haasnoot, M., Kwakkel, J. H., Walker, W. E., & ter Maat, J. (2013). Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world. Global Environmental Change, 23(2), 485-498. doi:10.1016/j.gloenvcha.2012.12.006Haasnoot, M., Middelkoop, H., Offermans, A., Beek, E. van, & Deursen, W. P. A. van. (2012). Exploring pathways for sustainable water management in river deltas in a changing environment. Climatic Change, 115(3-4), 795-819. doi:10.1007/s10584-012-0444-2Hallegatte, S. (2009). Strategies to adapt to an uncertain climate change. Global Environmental Change, 19(2), 240-247. doi:10.1016/j.gloenvcha.2008.12.003Hartford, D. N. D., & Baecher, G. B. (2004). Risk and uncertainty in dam safety. doi:10.1680/rauids.32705Harvey, H., Hall, J., & Peppé, R. (2011). Computational decision analysis for flood risk management in an uncertain future. Journal of Hydroinformatics, 14(3), 537-561. doi:10.2166/hydro.2011.055Hawkins, E., & Sutton, R. (2009). The Potential to Narrow Uncertainty in Regional Climate Predictions. Bulletin of the American Meteorological Society, 90(8), 1095-1108. doi:10.1175/2009bams2607.1Heal, G., & Millner, A. (2014). Reflections. Review of Environmental Economics and Policy, 8(1), 120-137. doi:10.1093/reep/ret023Jones, R. N. (2000). Climatic Change, 45(3/4), 403-419. doi:10.1023/a:1005551626280Kaplan, S. (1997). The Words of Risk Analysis. Risk Analysis, 17(4), 407-417. doi:10.1111/j.1539-6924.1997.tb00881.xKENDALL, M. G. (1938). A NEW MEASURE OF RANK CORRELATION. Biometrika, 30(1-2), 81-93. doi:10.1093/biomet/30.1-2.81Khatri, K., & Vairavamoorthy, K. (2011). A New Approach of Decision Making under Uncertainty for Selecting a Robust Strategy: A Case of Water Pipes Failure. Vulnerability, Uncertainty, and Risk. doi:10.1061/41170(400)116Kingston, D. G., Todd, M. C., Taylor, R. G., Thompson, J. R., & Arnell, N. W. (2009). Uncertainty in the estimation of potential evapotranspiration under climate change. Geophysical Research Letters, 36(20). doi:10.1029/2009gl040267Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., & Meehl, G. A. (2010). Challenges in Combining Projections from Multiple Climate Models. Journal of Climate, 23(10), 2739-2758. doi:10.1175/2009jcli3361.1Lempert, R. J., Groves, D. G., Popper, S. W., & Bankes, S. C. (2006). A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios. Management Science, 52(4), 514-528. doi:10.1287/mnsc.1050.0472Lempert, R., Popper, S., & Bankes, S. (2003). Shaping the Next One Hundred Years: New Methods for Quantitative, Long-Term Policy Analysis. doi:10.7249/mr1626Levitan, S., & Thomson, R. (2009). The Application of Expected-Utility Theory to the Choice of Investment Channels in a Defined-Contribution Retirement Fund. ASTIN Bulletin, 39(2), 615-647. doi:10.2143/ast.39.2.2044651Leyva López, J. C., & Alvarez Carrillo, P. A. (2014). Accentuating the rank positions in an agreement index with reference to a consensus order. International Transactions in Operational Research, 22(6), 969-995. doi:10.1111/itor.12146Lind, N. (2007). Discounting risks in the far future. Reliability Engineering & System Safety, 92(10), 1328-1332. doi:10.1016/j.ress.2006.09.001Luo, K., Xu, Y., Zhang, B., & Zhang, H. (2016). Creating an acceptable consensus ranking for group decision making. Journal of Combinatorial Optimization, 36(1), 307-328. doi:10.1007/s10878-016-0086-9Meila M. K. Phadnis A. Patterson and J. A. Bilmes. 2012. “Consensus ranking under the exponential model.” Preprint submitted June 20 2012. http://arxiv.org/abs/1206.5265.Miao, D. Y., Li, Y. P., Huang, G. H., Yang, Z. F., & Li, C. H. (2014). Optimization Model for Planning Regional Water Resource Systems under Uncertainty. Journal of Water Resources Planning and Management, 140(2), 238-249. doi:10.1061/(asce)wr.1943-5452.0000303Minville, M., Brissette, F., & Leconte, R. (2010). Impacts and Uncertainty of Climate Change on Water Resource Management of the Peribonka River System (Canada). Journal of Water Resources Planning and Management, 136(3), 376-385. doi:10.1061/(asce)wr.1943-5452.0000041Morales-Torres, A., Escuder-Bueno, I., Serrano-Lombillo, A., & Castillo Rodríguez, J. T. (2019). Dealing with epistemic uncertainty in risk-informed decision making for dam safety management. Reliability Engineering & System Safety, 191, 106562. doi:10.1016/j.ress.2019.106562Morales-Torres, A., Serrano-Lombillo, A., Escuder-Bueno, I., & Altarejos-García, L. (2016). The suitability of risk reduction indicators to inform dam safety management. Structure and Infrastructure Engineering, 1-12. doi:10.1080/15732479.2015.1136830Neumayer, E., & Barthel, F. (2011). Normalizing economic loss from natural disasters: A global analysis. Global Environmental Change, 21(1), 13-24. doi:10.1016/j.gloenvcha.2010.10.004New, M., & Hulme, M. (2000). Integrated Assessment, 1(3), 203-213. doi:10.1023/a:1019144202120Palmieri, A., Shah, F., & Dinar, A. (2001). Economics of reservoir sedimentation and sustainable management of dams. Journal of Environmental Management, 61(2), 149-163. doi:10.1006/jema.2000.0392Park, T., Kim, C., & Kim, H. (2013). Valuation of Drainage Infrastructure Improvement Under Climate Change Using Real Options. Water Resources Management, 28(2), 445-457. doi:10.1007/s11269-013-0492-zPate-Cornell, E. (2002). Risk and Uncertainty Analysis in Government Safety Decisions. Risk Analysis, 22(3), 633-646. doi:10.1111/0272-4332.00043Pittock, A. B., Jones, R. N., & Mitchell, C. D. (2001). Probabilities will help us plan for climate change. Nature, 413(6853), 249-249. doi:10.1038/35095194Roach, T., Kapelan, Z., Ledbetter, R., & Ledbetter, M. (2016). Comparison of Robust Optimization and Info-Gap Methods for Water Resource Management under Deep Uncertainty. Journal of Water Resources Planning and Management, 142(9), 04016028. doi:10.1061/(asce)wr.1943-5452.0000660Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., … Teuling, A. J. (2010). Investigating soil moisture–climate interactions in a changing climate: A review. Earth-Science Reviews, 99(3-4), 125-161. doi:10.1016/j.earscirev.2010.02.004Serrano-Lombillo A. A. Morales-Torres I. Escuder-Bueno and L. Altarejos-García. 2013. “Sharing experience for safe and sustainable water storage.” In Proc. 9th ICOLD European Club Symp. Bergamo Italy: Italian Committee on Large Dams.Spence, C. M., & Brown, C. M. (2018). Decision Analytic Approach to Resolving Divergent Climate Assumptions in Water Resources Planning. Journal of Water Resources Planning and Management, 144(9), 04018054. doi:10.1061/(asce)wr.1943-5452.0000939Street, R. B., & Nilsson, C. (2014). Introduction to the Use of Uncertainties to Inform Adaptation Decisions. Adapting to an Uncertain Climate, 1-16. doi:10.1007/978-3-319-04876-5_1Swart, R. ., Raskin, P., & Robinson, J. (2004). The problem of the future: sustainability science and scenario analysis. Global Environmental Change, 14(2), 137-146. doi:10.1016/j.gloenvcha.2003.10.002Walker, W., Haasnoot, M., & Kwakkel, J. (2013). Adapt or Perish: A Review of Planning Approaches for Adaptation under Deep Uncertainty. Sustainability, 5(3), 955-979. doi:10.3390/su5030955Walker, W. 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High-quality, high-throughput measurement of protein-DNA binding using HiTS-FLIP

In order to understand in more depth and on a genome wide scale the behavior of transcription factors (TFs), novel quantitative experiments with high-throughput are needed. Recently, HiTS-FLIP (High-Throughput Sequencing-Fluorescent Ligand Interaction Profiling) was invented by the Burge lab at the MIT (Nutiu et al. (2011)). Based on an Illumina GA-IIx machine for next-generation sequencing, HiTS-FLIP allows to measure the affinity of fluorescent labeled proteins to millions of DNA clusters at equilibrium in an unbiased and untargeted way examining the entire sequence space by Determination of dissociation constants (Kds) for all 12-mer DNA motifs. During my PhD I helped to improve the experimental design of this method to allow measuring the protein-DNA binding events at equilibrium omitting any washing step by utilizing the TIRF (Total Internal Reflection Fluorescence) based optics of the GA-IIx. In addition, I developed the first versions of XML based controlling software that automates the measurement procedure. Meeting the needs for processing the vast amount of data produced by each run, I developed a sophisticated, high performance software pipeline that locates DNA clusters, normalizes and extracts the fluorescent signals. Moreover, cluster contained k-mer motifs are ranked and their DNA binding affinities are quantified with high accuracy. My approach of applying phase-correlation to estimate the relative translative Offset between the observed tile images and the template images omits resequencing and thus allows to reuse the flow cell for several HiTS-FLIP experiments, which greatly reduces cost and time. Instead of using information from the sequencing images like Nutiu et al. (2011) for normalizing the cluster intensities which introduces a nucleotide specific bias, I estimate the cluster related normalization factors directly from the protein Images which captures the non-even illumination bias more accurately and leads to an improved correction for each tile image. My analysis of the ranking algorithm by Nutiu et al. (2011) has revealed that it is unable to rank all measured k-mers. Discarding all the clusters related to previously ranked k-mers has the side effect of eliminating any clusters on which k-mers could be ranked that share submotifs with previously ranked k-mers. This shortcoming affects even strong binding k-mers with only one mutation away from the top ranked k-mer. My findings show that omitting the cluster deletion step in the ranking process overcomes this limitation and allows to rank the full spectrum of all possible k-mers. In addition, the performance of the ranking algorithm is drastically reduced by my insight from a quadratic to a linear run time. The experimental improvements combined with the sophisticated processing of the data has led to a very high accuracy of the HiTS-FLIP dissociation constants (Kds) comparable to the Kds measured by the very sensitive HiP-FA assay (Jung et al. (2015)). However, experimentally HiTS-FLIP is a very challenging assay. In total, eight HiTS-FLIP experiments were performed but only one showed saturation, the others exhibited Protein aggregation occurring at the amplified DNA clusters. This biochemical issue could not be remedied. As example TF for studying the details of HiTS-FLIP, GCN4 was chosen which is a dimeric, basic leucine zipper TF and which acts as the master regulator of the amino acid starvation Response in Saccharomyces cerevisiae (Natarajan et al. (2001)). The fluorescent dye was mOrange. The HiTS-FLIP Kds for the TF GCN4 were validated by the HiP-FA assay and a Pearson correlation coefficient of R=0.99 and a relative error of delta=30.91% was achieved. Thus, a unique and comprehensive data set of utmost quantitative precision was obtained that allowed to study the complex binding behavior of GCN4 in a new way. My Downstream analyses reveal that the known 7-mer consensus motif of GCN4, which is TGACTCA, is modulated by its 2-mer neighboring flanking regions spanning an affinity range over two orders of magnitude from a Kd=1.56 nM to Kd=552.51 nM. These results suggest that the common 9-mer PWM (Position Weight Matrix) for GCN4 is insufficient to describe the binding behavior of GCN4. Rather, an additional left and right flanking nucleotide is required to extend the 9-mer to an 11-mer. My analyses regarding mutations and related delta delta G values suggest long-range interdependencies between nucleotides of the two dimeric half-sites of GCN4. Consequently, models assuming positional independence, such as a PWM, are insufficient to explain these interdependencies. Instead, the full spectrum of affinity values for all k-mers of appropriate size should be measured and applied in further analyses as proposed by Nutiu et al. (2011). Another discovery were new binding motifs of GCN4, which can only be detected with a method like HiTS-FLIP that examines the entire sequence space and allows for unbiased, de-novo motif discovery. All These new motifs contain GTGT as a submotif and the data collected suggests that GCN4 binds as monomer to these new motifs. Therefore, it might be even possible to detect different binding modes with HiTS-FLIP. My results emphasize the binding complexity of GCN4 and demonstrate the advantage of HiTS-FLIP for investigating the complexity of regulative processes

Equity in healthcare financing in Italy

This Ph.D. dissertation discusses equity in healthcare financing in Italy. Each of the three chapters of this dissertation constitutes an independent research output, answering stand-alone research questions. Chapter 1 is a systematic review on equity in healthcare financing in OECD and non- OECD countries. This deals both with the methodology used and the evidence around the world on vertical equity in healthcare financing. Chapters 2 and 3 report empirical evidence from Italy. Chapter 2 analyses the progressivity of healthcare financing in the Italian system and focuses on Italian regions, performing a comparison of progressivity of the healthcare financing across regional systems. Chapter 3 provides an assessment of how differences in co-payments between the Italian regions contribute to growing inequalities in access to public health care services in Italy. A common ground among chapters is the measurement of equity and inequalities in health financing, with particular reference to differences among the Italian regions

High-quality, high-throughput measurement of protein-DNA binding using HiTS-FLIP

In order to understand in more depth and on a genome wide scale the behavior of transcription factors (TFs), novel quantitative experiments with high-throughput are needed. Recently, HiTS-FLIP (High-Throughput Sequencing-Fluorescent Ligand Interaction Profiling) was invented by the Burge lab at the MIT (Nutiu et al. (2011)). Based on an Illumina GA-IIx machine for next-generation sequencing, HiTS-FLIP allows to measure the affinity of fluorescent labeled proteins to millions of DNA clusters at equilibrium in an unbiased and untargeted way examining the entire sequence space by Determination of dissociation constants (Kds) for all 12-mer DNA motifs. During my PhD I helped to improve the experimental design of this method to allow measuring the protein-DNA binding events at equilibrium omitting any washing step by utilizing the TIRF (Total Internal Reflection Fluorescence) based optics of the GA-IIx. In addition, I developed the first versions of XML based controlling software that automates the measurement procedure. Meeting the needs for processing the vast amount of data produced by each run, I developed a sophisticated, high performance software pipeline that locates DNA clusters, normalizes and extracts the fluorescent signals. Moreover, cluster contained k-mer motifs are ranked and their DNA binding affinities are quantified with high accuracy. My approach of applying phase-correlation to estimate the relative translative Offset between the observed tile images and the template images omits resequencing and thus allows to reuse the flow cell for several HiTS-FLIP experiments, which greatly reduces cost and time. Instead of using information from the sequencing images like Nutiu et al. (2011) for normalizing the cluster intensities which introduces a nucleotide specific bias, I estimate the cluster related normalization factors directly from the protein Images which captures the non-even illumination bias more accurately and leads to an improved correction for each tile image. My analysis of the ranking algorithm by Nutiu et al. (2011) has revealed that it is unable to rank all measured k-mers. Discarding all the clusters related to previously ranked k-mers has the side effect of eliminating any clusters on which k-mers could be ranked that share submotifs with previously ranked k-mers. This shortcoming affects even strong binding k-mers with only one mutation away from the top ranked k-mer. My findings show that omitting the cluster deletion step in the ranking process overcomes this limitation and allows to rank the full spectrum of all possible k-mers. In addition, the performance of the ranking algorithm is drastically reduced by my insight from a quadratic to a linear run time. The experimental improvements combined with the sophisticated processing of the data has led to a very high accuracy of the HiTS-FLIP dissociation constants (Kds) comparable to the Kds measured by the very sensitive HiP-FA assay (Jung et al. (2015)). However, experimentally HiTS-FLIP is a very challenging assay. In total, eight HiTS-FLIP experiments were performed but only one showed saturation, the others exhibited Protein aggregation occurring at the amplified DNA clusters. This biochemical issue could not be remedied. As example TF for studying the details of HiTS-FLIP, GCN4 was chosen which is a dimeric, basic leucine zipper TF and which acts as the master regulator of the amino acid starvation Response in Saccharomyces cerevisiae (Natarajan et al. (2001)). The fluorescent dye was mOrange. The HiTS-FLIP Kds for the TF GCN4 were validated by the HiP-FA assay and a Pearson correlation coefficient of R=0.99 and a relative error of delta=30.91% was achieved. Thus, a unique and comprehensive data set of utmost quantitative precision was obtained that allowed to study the complex binding behavior of GCN4 in a new way. My Downstream analyses reveal that the known 7-mer consensus motif of GCN4, which is TGACTCA, is modulated by its 2-mer neighboring flanking regions spanning an affinity range over two orders of magnitude from a Kd=1.56 nM to Kd=552.51 nM. These results suggest that the common 9-mer PWM (Position Weight Matrix) for GCN4 is insufficient to describe the binding behavior of GCN4. Rather, an additional left and right flanking nucleotide is required to extend the 9-mer to an 11-mer. My analyses regarding mutations and related delta delta G values suggest long-range interdependencies between nucleotides of the two dimeric half-sites of GCN4. Consequently, models assuming positional independence, such as a PWM, are insufficient to explain these interdependencies. Instead, the full spectrum of affinity values for all k-mers of appropriate size should be measured and applied in further analyses as proposed by Nutiu et al. (2011). Another discovery were new binding motifs of GCN4, which can only be detected with a method like HiTS-FLIP that examines the entire sequence space and allows for unbiased, de-novo motif discovery. All These new motifs contain GTGT as a submotif and the data collected suggests that GCN4 binds as monomer to these new motifs. Therefore, it might be even possible to detect different binding modes with HiTS-FLIP. My results emphasize the binding complexity of GCN4 and demonstrate the advantage of HiTS-FLIP for investigating the complexity of regulative processes

Is education a solution to inequality? A comparison on how people perceive educational inequality and social mobility in Hong Kong

This paper would examine how people in Hong Kong perceive the correlation between educational inequality and social mobility, using public opinion as the major research approach. An opinion survey is conducted to obtain the overall attitude towards educational inequality and social mobility, followed by comparisons on the social issues using official statistics and figures as published by the government and academia. The paper would compare the differences between how the general public perceive the issue of educational inequality and social mobility as reflected in the opinion survey, and the reality of the issues as presented by official data