Understanding Terrorism from an Economic perspective

Terrorism has emerged as a major threat to the contemporary society. Nation States are reliant on their counter-terrorism laws for checking terrorism and deterring terrorists. To understand the effectiveness of these counter terrorism laws, it is important to first understand the behaviour of terrorists, so as to comprehend what actions can dissuade terrorist’s behaviour and decision to propagate violence. This paper will first look at behaviour of terrorist in decision making from an economic perspective, then will try to explore if there are any economic determinant of terrorism and finally, since the cost of terrorism is huge in terms of life, property etc, will discuss the status of counter-terrorism legislations in India. In the study it has been shown that terrorists are rational in decision making. It has also been found that economic determinants are not significant in determining terrorism; however, to a certain extent education does have a positive relation with participation in terrorism. Terrorism has huge cost, and hence it is essential to have counter-terrorist legislations. These legislations provides power to the state to deny operating space to terrorists and their supporters, deter them from carrying out terrorist acts, ensure the basic rights of the people, and uphold the Fundamental Rights enshrined in the Constitution

Peri-Urban Villages of Bangalore, India

1 Backdrop Mundur is a village spanning an area of 1,302 acres. This village is located towards the east of Bangalore, India, 26 km away from the city’s centre. Prior to the 1980s, the village had a historically diverse set of common property resources and common grazing grounds, water bodies and tree groves accounting for roughly 27% of the village’s geographic area. The village enjoys an average annual precipitation of about 720 mm and is vulnerable to periodic droughts. Traditionally, it w..

The impact of adjusting for baseline in pharmacogenomic genome-wide association studies of quantitative change.

In pharmacogenomic studies of quantitative change, any association between genetic variants and the pretreatment (baseline) measurement can bias the estimate of effect between those variants and drug response. A putative solution is to adjust for baseline. We conducted a series of genome-wide association studies (GWASs) for low-density lipoprotein cholesterol (LDL-C) response to statin therapy in 34,874 participants of the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort as a case study to investigate the impact of baseline adjustment on results generated from pharmacogenomic studies of quantitative change. Across phenotypes of statin-induced LDL-C change, baseline adjustment identified variants from six loci meeting genome-wide significance (SORT/CELSR2/PSRC1, LPA, SLCO1B1, APOE, APOB, and SMARCA4/LDLR). In contrast, baseline-unadjusted analyses yielded variants from three loci meeting the criteria for genome-wide significance (LPA, APOE, and SLCO1B1). A genome-wide heterogeneity test of baseline versus statin on-treatment LDL-C levels was performed as the definitive test for the true effect of genetic variants on statin-induced LDL-C change. These findings were generally consistent with the models not adjusting for baseline signifying that genome-wide significant hits generated only from baseline-adjusted analyses (SORT/CELSR2/PSRC1, APOB, SMARCA4/LDLR) were likely biased. We then comprehensively reviewed published GWASs of drug-induced quantitative change and discovered that more than half (59%) inappropriately adjusted for baseline. Altogether, we demonstrate that (1) baseline adjustment introduces bias in pharmacogenomic studies of quantitative change and (2) this erroneous methodology is highly prevalent. We conclude that it is critical to avoid this common statistical approach in future pharmacogenomic studies of quantitative change

Designing Sustainable Urban Futures

Many 21st century cities have the potential to be sustainable and resource-saving living spaces when multifunctional structures, well-integrated transportation infrastructure, and democratic governance processes are present. Sustainable urban futures require a focus on the needs of humans and environmental best practices, as well as on the creative scope for community-driven sustainability innovations. This book is based on contributions from science and practice to the international symposium on “Sustainable Urban Development at Different Scales” organized by the Institute for Technology Assessment and Systems Analysis at the Karlsruhe Institute of Technology, Karlsruhe, Germany, in May 2014. The symposium used the global urbanization and reurbanization trend as an opportunity to examine cities as sustainable living spaces. This book identifi es concepts, analytic approaches, and practical applications for the design of sustainable urban futures among multiple disciplines and cultural backgrounds.Viele Städte des 21. Jahrhunderts haben das Potenzial, ein nachhaltiger und ressourcenschonender Lebensraum zu sein, wenn multifunktionale Strukturen, eine gut integrierte Verkehrsinfrastruktur und demokratische Stadtentwicklungsprozesse gegeben sind. Nachhaltige Stadtzukünfte erfordern einen starken Fokus auf die Berücksichtigung menschlicher Bedürfnissen an ihren Lebensraum, auf Umweltfreundlichkeit und Gesundheit sowie die gemeinsame Gestaltung kreativer Freiräume für nachhaltige Praktiken. Diese Buch basiert auf Beiträgen aus Wissenschaft und Praxis zum internationalen Symposium „Sustainable Urban Development at Different Scales“, das im Mai 2014 am Institut für Technikfolgenabschätzung und Systemanalyse am Karlsruher Institut für Technologie stattfand. Das Symposium nahm den globalen Urbanisierungsund Reurbanisierungstrend zum Anlass, um Städte auf unterschiedlichen Maßstabsebenen als nachhaltige Lebensräume zu diskutieren. Dieses Buch bietet Analysen, Konzepte und Ansätze zur Gestaltung nachhaltiger Stadtzukünfte aus der Sicht multipler Disziplinen und vor unterschiedlichen kulturellen Hintergründen

Power comparison between population-based case-control studies and family-based transmission-disequilibrium tests: An empirical study

Background: There are two major classes of genetic association analyses: population based and family based. Population-based case-control studies have been the method of choice due to the ease of data collection. However, population stratification is one of the major limitations of case-control studies, while family-based studies are protected against stratification. In this study, we carry out extensive simulations under different disease models (both Mendelian as well as complex) to evaluate the relative powers of the two approaches in detecting association. Materials and Methods: The power comparisons are based on a case-control design comprising 200 cases and 200 controls versus a Transmission Disequilibrium Test (TDT) or Pedigree Disequilibrium Test (PDT) design with 200 informative trios. We perform the allele-level test for case-control studies, which is based on the difference of allele frequencies at a single nucleotide polymorphism (SNP) between unrelated cases and controls. The TDT and the PDT are based on preferential allelic transmissions at a SNP from heterozygous parents to the affected offspring. We considered five disease modes of inheritance: (i) recessive with complete penetrance (ii) dominant with complete penetrance and (iii), (iv) and (v) complex diseases with varying levels of penetrances and phenocopies. Results: We find that while the TDT/PDT design with 200 informative trios is in general more powerful than a case-control design with 200 cases and 200 controls (except when the heterozygosity at the marker locus is high), it may be necessary to sample a very large number of trios to obtain the requisite number of informative families. Conclusion: The current study provides insights into power comparisons between population-based and family-based association studies

Determining Which Phenotypes Underlie a Pleiotropic Signal

Discovering pleiotropic loci is important to understand the biological basis of seemingly distinct phenotypes. Most methods for assessing pleiotropy only test for the overall association between genetic variants and multiple phenotypes. To determine which specific traits are pleiotropic, we evaluate via simulation and application three different strategies. The first is model selection techniques based on the inverse regression of genotype on phenotypes. The second is a subset-based meta analysis ASSET [Bhattacharjee et al., ], which provides an optimal subset of nonnull traits. And the third is a modified Benjamini-Hochberg (B-H) procedure of controlling the expected false discovery rate [Benjamini and Hochberg, ] in the framework of phenome-wide association study. From our simulations we see that an inverse regression-based approach MultiPhen [O'Reilly et al., ] is more powerful than ASSET for detecting overall pleiotropic association, except for when all the phenotypes are associated and have genetic effects in the same direction. For determining which specific traits are pleiotropic, the modified B-H procedure performs consistently better than the other two methods. The inverse regression-based selection methods perform competitively with the modified B-H procedure only when the phenotypes are weakly correlated. The efficiency of ASSET is observed to lie below and in between the efficiency of the other two methods when the traits are weakly and strongly correlated, respectively. In our application to a large GWAS, we find that the modified B-H procedure also performs well, indicating that this may be an optimal approach for determining the traits underlying a pleiotropic signal

An efficient Bayesian meta-analysis approach for studying cross-phenotype genetic associations

<div><p>Simultaneous analysis of genetic associations with multiple phenotypes may reveal shared genetic susceptibility across traits (pleiotropy). For a locus exhibiting overall pleiotropy, it is important to identify which specific traits underlie this association. We propose a Bayesian meta-analysis approach (termed CPBayes) that uses summary-level data across multiple phenotypes to simultaneously measure the evidence of aggregate-level pleiotropic association and estimate an optimal subset of traits associated with the risk locus. This method uses a unified Bayesian statistical framework based on a spike and slab prior. CPBayes performs a fully Bayesian analysis by employing the Markov Chain Monte Carlo (MCMC) technique Gibbs sampling. It takes into account heterogeneity in the size and direction of the genetic effects across traits. It can be applied to both cohort data and separate studies of multiple traits having overlapping or non-overlapping subjects. Simulations show that CPBayes can produce higher accuracy in the selection of associated traits underlying a pleiotropic signal than the subset-based meta-analysis ASSET. We used CPBayes to undertake a genome-wide pleiotropic association study of 22 traits in the large Kaiser GERA cohort and detected six independent pleiotropic loci associated with at least two phenotypes. This includes a locus at chromosomal region 1q24.2 which exhibits an association simultaneously with the risk of five different diseases: Dermatophytosis, Hemorrhoids, Iron Deficiency, Osteoporosis and Peripheral Vascular Disease. We provide an R-package ‘CPBayes’ implementing the proposed method.</p></div

Power comparison between population-based case–control studies and family-based transmission–disequilibrium tests: An empirical study

Background: There are two major classes of genetic association analyses: population based and family based. Population-based case-control studies have been the method of choice due to the ease of data collection. However, population stratification is one of the major limitations of case-control studies, while family-based studies are protected against stratification. In this study, we carry out extensive simulations under different disease models (both Mendelian as well as complex) to evaluate the relative powers of the two approaches in detecting association. Materials and Methods: The power comparisons are based on a case-control design comprising 200 cases and 200 controls versus a Transmission Disequilibrium Test (TDT) or Pedigree Disequilibrium Test (PDT) design with 200 informative trios. We perform the allele-level test for case-control studies, which is based on the difference of allele frequencies at a single nucleotide polymorphism (SNP) between unrelated cases and controls. The TDT and the PDT are based on preferential allelic transmissions at a SNP from heterozygous parents to the affected offspring. We considered five disease modes of inheritance: (i) recessive with complete penetrance (ii) dominant with complete penetrance and (iii), (iv) and (v) complex diseases with varying levels of penetrances and phenocopies. Results: We find that while the TDT/PDT design with 200 informative trios is in general more powerful than a case-control design with 200 cases and 200 controls (except when the heterozygosity at the marker locus is high), it may be necessary to sample a very large number of trios to obtain the requisite number of informative families. Conclusion: The current study provides insights into power comparisons between population-based and family-based association studies