Reliability assessment of null allele detection: inconsistencies between and within different methods

Microsatellite loci are widely used in population genetic studies, but the presence of null alleles may lead to biased results. Here we assessed five methods that indirectly detect null alleles, and found large inconsistencies among them. Our analysis was based on 20 microsatellite loci genotyped in a natural population of Microtus oeconomus sampled during 8 years, together with 1200 simulated populations without null alleles, but experiencing bottlenecks of varying duration and intensity, and 120 simulated populations with known null alleles. In the natural population, 29% of positive results were consistent between the methods in pairwise comparisons, and in the simulated dataset this proportion was 14%. The positive results were also inconsistent between different years in the natural population. In the null-allele-free simulated dataset, the number of false positives increased with increased bottleneck intensity and duration. We also found a low concordance in null allele detection between the original simulated populations and their 20% random subsets. In the populations simulated to include null alleles, between 22% and 42% of true null alleles remained undetected, which highlighted that detection errors are not restricted to false positives. None of the evaluated methods clearly outperformed the others when both false positive and false negative rates were considered. Accepting only the positive results consistent between at least two methods should considerably reduce the false positive rate, but this approach may increase the false negative rate. Our study demonstrates the need for novel null allele detection methods that could be reliably applied to natural population

Software cost estimation

The paper gives an overview of the state of the art of software cost estimation (SCE). The main questions to be answered in the paper are: (1) What are the reasons for overruns of budgets and planned durations? (2) What are the prerequisites for estimating? (3) How can software development effort be estimated? (4) What can software project management expect from SCE models, how accurate are estimations which are made using these kind of models, and what are the pros and cons of cost estimation models

Investigating farmers' preferences for the design of agri-environment schemes: a choice experiment approach

In recent decades agri-environment schemes (AES) have become an increasingly important tool for policy makers aiming to reverse the post-war decline in environmental quality on agricultural land. The voluntary nature of such schemes means that the decision of farmers to participate is central to achieving policy objectives. Therefore, this paper uses a choice experiment approach to investigate the role that scheme design can have on encouraging farmers to participate. Choice data was gathered from a survey of farmers in 10 case study areas across the EU and analysed using both mixed logit and latent class models. In general, farmers were found to require greater financial incentives to join schemes with longer contracts or that offer less flexibility or higher levels of paperwork. It was also observed that a large segment of farmers ('low resistance adopters') would be willing to accept relatively small incentive payments for their participation in schemes offering relatively little flexibility and high levels of additional paperwork, when compared to a contrasting segment of 'high resistance adopters'. © 2009 University of Newcastle upon Tyne

A Bayesian inference framework to reconstruct transmission trees using epidemiological and genetic data

The accurate identification of the route of transmission taken by an infectious agent through a host population is critical to understanding its epidemiology and informing measures for its control. However, reconstruction of transmission routes during an epidemic is often an underdetermined problem: data about the location and timings of infections can be incomplete, inaccurate, and compatible with a large number of different transmission scenarios. For fast-evolving pathogens like RNA viruses, inference can be strengthened by using genetic data, nowadays easily and affordably generated. However, significant statistical challenges remain to be overcome in the full integration of these different data types if transmission trees are to be reliably estimated. We present here a framework leading to a bayesian inference scheme that combines genetic and epidemiological data, able to reconstruct most likely transmission patterns and infection dates. After testing our approach with simulated data, we apply the method to two UK epidemics of Foot-and-Mouth Disease Virus (FMDV): the 2007 outbreak, and a subset of the large 2001 epidemic. In the first case, we are able to confirm the role of a specific premise as the link between the two phases of the epidemics, while transmissions more densely clustered in space and time remain harder to resolve. When we consider data collected from the 2001 epidemic during a time of national emergency, our inference scheme robustly infers transmission chains, and uncovers the presence of undetected premises, thus providing a useful tool for epidemiological studies in real time. The generation of genetic data is becoming routine in epidemiological investigations, but the development of analytical tools maximizing the value of these data remains a priority. Our method, while applied here in the context of FMDV, is general and with slight modification can be used in any situation where both spatiotemporal and genetic data are available

ERP inside Large Organizations

Many large companies in Romania are still functioning without an ERP system. Instead they are using traditional application systems built around the strong boundaries of specific functions: finance, selling, HR, production. An ERP will offer lots of advantages among which the integration of functionalities and support for top management decisions. Although the total cost of ownership is not small and there are some risks when implementing an ERP inside large and very large organizations, having such a system is mandatory. Choosing the right product and vendor and using a correct risk management strategy, will ensure a successful implementation.Enterprise Functions, ERP Functionalities, Process Lines and Solutions, Cost Implementation, Total Cost of Ownership, Risk Management, Active Global Support, ERP Advantages, Success Factors, Return of Investment

When costs from being a constraint become a driver for concept generation

Managing innovation requires solving issues related to the internal development and engineering processes of a company (supply side), in addition to facing the market and competition (demand side). In this context, the product development process is crucial, as different tradeoffs and issues that require managerial attention tend to arise. The main challenges result in managers requiring practical support tools that can help them in planning and controlling the process, and of designers requiring them for supporting their design decisions. Hence, the thesis aims to focus on product costs to understand its influence on design decisions as well as on the overall management of the product development process. The core part of the thesis is based on the models and methods developed for enhancing cost analysis at the beginning of the product development process. This investigation aims to determine the importance of cost estimation in improving the overall performance of a newly designed product. The focus on post-sales and, more generally, on the customer, has become so relevant that manufacturers have to take into account not only the most obvious aspects about the product and related services, but even consider the associated implications for customers during product use. However, implementing a product life cycle perspective is still a challenging process for companies. From a methodological perspective, the reasons include uncertainty regarding the available approaches and ambiguity about their application. In terms of implementation, the main challenge is the long-term cost management, when one considers uncertainty in process duration, data collection, and other supply chain issues. In fact, helping designers and managers efficiently understand the strategic and operational consequences of a cost analysis implementation is still a problem, although advanced methodologies for more in-depth and timely analyses are available. And this is even more if one considers that product lifecycle represents a critical area of investment, particularly in light of the new challenges and opportunities provided by big data analysis in the Industry 4.0 contexts. This dissertation addresses these aspects and provides a methodological approach to assess a rigorous implementation of life-cycle cost while discussing the evidence derived from its operational and strategic impacts. The novelty lies in the way the data and information are collected, dynamically moving the focus of the investigation with regard to the data aggregation level and the product structure. The way the techniques have been combined represents a further aspect of novelty. In fact, the introduced approach contributes to a new trend in the Product Cost Estimation (PCE) literature, which suggests the integration of different techniques for product life-cycle cost analysis. The findings obtained at the end of the process can be employed to assess the impact of platform design strategy and variety proliferations on the total life-cycle costs. By evaluating the possible mix of options, and hence offering the optimal product configuration, a more conscious way for planning the product portfolio has been provided. In this sense, a detailed operational analysis (as the cost estimation) is used to inform and drive the strategic planning of the portfolio. Finally, the thesis discusses the future opportunities and challenges for product cost analysis, assessing how digitalisation of manufacturing operations may affect the data gathering and analysis process. In this new environment, the opportunity for a more informed, cost-driven decision-making will multiply, leading to varied opportunities in this research field

Leveraging population admixture to characterize the heritability of complex traits.

Despite recent progress on estimating the heritability explained by genotyped SNPs (h(2)g), a large gap between h(2)g and estimates of total narrow-sense heritability (h(2)) remains. Explanations for this gap include rare variants or upward bias in family-based estimates of h(2) due to shared environment or epistasis. We estimate h(2) from unrelated individuals in admixed populations by first estimating the heritability explained by local ancestry (h(2)γ). We show that h(2)γ = 2FSTCθ(1 - θ)h(2), where FSTC measures frequency differences between populations at causal loci and θ is the genome-wide ancestry proportion. Our approach is not susceptible to biases caused by epistasis or shared environment. We applied this approach to the analysis of 13 phenotypes in 21,497 African-American individuals from 3 cohorts. For height and body mass index (BMI), we obtained h(2) estimates of 0.55 ± 0.09 and 0.23 ± 0.06, respectively, which are larger than estimates of h(2)g in these and other data but smaller than family-based estimates of h(2)

Meta-Heuristics Analysis for Technologically Complex Programs: Understanding the Impact of Total Constraints for Schedule, Quality and Cost

Program management data associated with a technically complex radio frequency electronics base communication system has been collected and analyzed to identify heuristics which may be utilized in addition to existing processes and procedures to provide indicators that a program is trending to failure. Analysis of the collected data includes detailed schedule analysis, detailed earned value management analysis and defect analysis within the framework of a Firm Fixed Price (FFP) incentive fee contract. This project develops heuristics and provides recommendations for analysis of complex project management efforts such as those discussed herein. The analysis of the effects of the constraints on management of the program indicate that, unless unambiguous program management controls are applied very early to milestone execution and risk management, then plans, schedules, tasks, and resource allocation will not be successful in controlling the constraints of schedule, quality or cost