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Animal production is still a major UK industry of about £10 billion and like all aspects of agriculture faces a number of challenges. The biotechnology revolution is also sweeping through the industry with a range of major emerging technologies becoming available. Describes these technologies and discusses their potential impact on the agro‐food industry and the public. Although the issues raised appear at first sight novel, they can be regulated under the current framework available within the UK; public concern over some of the technologies is genuine but in most cases can be answered. Currently research in the UK puts this country at the forefront of these technologies and the emerging commercial opportunities must not be lost.

The products of transgenic technology have captured the attention of enthusiasts and detractors, but transgenics are just one tool of agricultural biotechnology. Other applications enable scientists to understand biodiversity, to track genes through generations in breeding programs, and to move genes among closely related as well as unrelated organisms. These applications all have the potential to lead to substantial productivity gains.

In this chapter we provide an introduction to basic plant genetic concepts, defining molecular markers, transgenic and cisgenic techniques. We briefly summarize the status of commercialized biotechnology applications to agriculture. We consider the likely future commercialization of products like drought tolerant crops, crops designed to improve human nutrition, pharmaceuticals from transgenic plants, biofuels, and crops for environmental remediation. We identify genomic selection as a potentially powerful new technique and conclude with our reflections on the state of agricultural biotechnology.

Research at universities and other public-sector institutions, largely focused on advancing knowledge, has aroused enormous optimism about the promise of these DNA-based technologies. This in turn has led to large private-sector investments on maize, soybean, canola, and cotton, with wide adoption of the research products in about eight countries. Much has been made of the potential of biotechnology to address food needs in the low-income countries, and China, India, and Brazil have large public DNA-based crop variety development efforts. But other lower income developing countries have little capability to use these tools, even the most straightforward marker applications. Ensuring that these and other applications of biotechnology lead to products that are well adapted to local agriculture requires adaptive research capacity that is lacking in the lowest income, most food-insecure nations. We are less optimistic than many others that private research will fund these needs.

A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose.

In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC.

The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm.

As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

This chapter explores the rise in genetic approaches to health disparities at the turn of the twenty-first century.

Analysis of public health policies, genome project records, ethnography of project leaders and leading genetic epidemiologists, and news coverage of international projects demonstrates how the study of health disparities and genetic causes of health simultaneously took hold just as the new field of genomics and matters of racial inequality became a global priority for biomedical science and public health.

As the U.S. federal government created policies to implement racial inclusion standards, international genome projects seized the study race, and diseases that exhibit disparities by race. Genomic leaders made health disparities research a central feature of their science. However, recent attempts to move toward analysis of gene-environment interactions in health and disease have proven insufficient in addressing sociological contributors to health disparities. In place of in-depth analyses of environmental causes, pharmacogenomics drugs, diagnostics, and inclusion in sequencing projects have become the frontline solutions to health disparities.

The chapter argues that genetic forms of medicalization and racialization have taken hold over science and public health around the world, thereby engendering a divestment from sociological approaches that do not align with the expansion of genomic science. The chapter thus contributes to critical discussions in the social and health sciences about the fundamental processes of medicalization, racialization, and geneticization in contemporary society.

Personalized medicine (PM) encompasses a set of procedures, technologies and medications; the term became more prominent from the 2000s onwards and stems from the mapping of the human genome. The purposes of this study were to analyse the development stage of the process of technological innovation for PM and the obstacles that prevent PM from being adopted in the public health system in Brazil.

As a research method, this paper opts for a case study carried out at the Hospital das Clínicas, which belongs to São Paulo Medical School. In total, 22 in-depth interviews were carried out at the hospital to identify current practices in PM, future prospects and barriers imposed to the adoption of PM technologies in public health.

Personalized or precision medicine is already a reality for a small portion of the Brazilian population and is gradually gaining ground in public health care. One finding is that such changes are occurring in a disjointed manner in an incomplete and under development health innovation system. The analysis pointed out that the obstacles identified in Brazil are the same as those faced by high-income countries such as regulation, lack of clinical studies and need to adapt clinical studies to PM. They appear in all stages of the innovation cycle, from research to widespread use.

The research method was a case study, so the findings cannot be extrapolated to other contexts. A limited number of professionals were interviewed, their opinions may not reflect those of their organizations.

There are several studies that discuss how health-care systems in high-income countries could incorporate these new technologies, but only a few focuses on low or middle-income countries such as Brazil.

The use of sequence and structure databanks is examined in relation to their application in some of the main branches of protein studies. Also the question of availability is addressed by means of presenting some information on current sequence and structure databanks. Increasingly research in molecular science requires joint access to both sequence and structure databases, and the reasons for this development, together with some of the methods for integrated access, are analysed.

In recent years, the field of comparative and international education (CIE) has experienced an outburst of self-reflective papers wherein comparativists study the nature of the field and map its content. This study contributes to this trend by drawing attention to a previously unstudied aspect of CIE: its purpose. Using Arnove’s dimensions as a starting point to create five new purpose categories, four prominent CIE journals are surveyed to test whether the pragmatic history of CIE is evident in its current body of research. In this process, a complete and clear genetic mapping of the journals is created, which explores their similarities and differences, as well as the changes in their content over time. Findings indicate that the pragmatic purpose of CIE dominates, though it is primarily emancipatory and transformative in its prescription. Furthermore, articles rooted in specific situational contexts were more prominent than expected considering the comparative and international nature of the field.

Resource selection/optimization problems are often characterized by two related problems: numerical function and combinatorial optimization. Although techniques ranging from classical mathematical programming to knowledge‐based expert systems (KBESs) have been applied to solve the function optimization problem, there still exists the need for improved solution techniques in solving the combinatorial optimization. This paper reports an exploratory work that investigates the integration of genetic algorithms (GAs) with organizational databases to solve the combinatorial problem in resource optimization and management. The solution strategy involved using two levels of knowledge (declarative and procedural) to address the problems of numerical function, and combinatorial optimization of resources. The research shows that GAs can be effectively integrated into the evolving decision support systems (DSSs) for resource optimization and management, and that integrating a hybrid GA that incorporates resource economic and productivity factors, would facilitate the development of a more robust DSS. This helps to overcome the major limitations of current optimization techniques such as linear programming and monolithic techniques such as the KBES. The results also highlighted that GA exhibits the chaotic characteristics that are often observed in other complex non‐linear dynamic systems. The empirical results are discussed, and some recommendations given on how to achieve improved results in adapting GAs for decision support in the architecture, engineering and construction (AEC) sector.

This chapter proposes an optimized innovative information technology as a means for achieving operational functionalities of real-time portable electronic health records, system interoperability, longitudinal health-risks research cohort and surveillance of adverse events infrastructure, and clinical, genome regions – disease and interventional prevention infrastructure. In application to the Dod-VA (Department of Defense and Veteran's Administration) health information systems, the proposed modernization can be carried out as an “add-on” expansion (estimated at $288 million in constant dollars) or as a “stand-alone” innovative information technology system (estimated at $489.7 million), and either solution will prototype an infrastructure for nation-wide health information systems interoperability, portable real-time electronic health records (EHRs), adverse events surveillance, and interventional prevention based on targeted single nucleotide polymorphisms (SNPs) discovery.

Applies a computer model GAIA (Groups of Adaptive Inferencing Agents) to simulate the lifecycle of artificial groups directed by agendas which specify varying strategies for collective problem solving. Within GAIA, groups of artificial agents dynamically learn and interact by proposing, combining and testing inductive hypotheses in the form of genetic building blocks. Agents share and combine building block solutions to evolve decision trees to respond to environmental inputs. Effects of agendas which emphasize stages of conservative and liberal problem solving strategies over a group’s lifecycle were simulated. Conservative strategies emphasize consensus and collective memories within groups. Liberal strategies emphasize challenges to collective memory and individual agent predictions. Agendas which vary from conservative to liberal resulted in the poor group solutions. Significantly better group solutions were produced by an agenda varying from liberal to conservative and back to liberal (L‐C‐L). The L‐C‐L agenda focuses on critical evaluation and rewards for individual contribution in the beginning and ending lifecycle stages and provides a middle stage of collective exploration.