Genome signatures, self-organizing maps and higher order phylogenies: a parametric analysis

Genome signatures are data vectors derived from the compositional statistics of DNA. The self-organizing map (SOM) is a neural network method for the conceptualisation of relationships within complex data, such as genome signatures. The various parameters of the SOM training phase are investigated for their effect on the accuracy of the resulting output map. It is concluded that larger SOMs, as well as taking longer to train, are less sensitive in phylogenetic classification of unknown DNA sequences. However, where a classification can be made, a larger SOM is more accurate. Increasing the number of iterations in the training phase of the SOM only slightly increases accuracy, without improving sensitivity. The optimal length of the DNA sequence k-mer from which the genome signature should be derived is 4 or 5, but shorter values are almost as effective. In general, these results indicate that small, rapidly trained SOMs are generally as good as larger, longer trained ones for the analysis of genome signatures. These results may also be more generally applicable to the use of SOMs for other complex data sets, such as microarray data

Self-Organizing Maps to Analyze Value Creation in Mergers and Acquisitions in the Telecommunications Sector

A great effort has been made in recent years to refine the study methods that emerged in the 1990s to assess long-term abnormal returns in the stock markets as a way to evaluate the value creation or destruction of merger and acquisition (M&A) in the sector of telecommunications. It is regularly addressed in generic merger and acquisition studies, with a short-term time horizon or just with a qualitative focus. In this work, we use a visual data-mining tool, Self-Organizing-Maps (SOM), to analyze mergers and acquisitions in telecommunications sector. The relationship among variables influencing the M&A was only observed due to the capabilities of the visual neural map method that allow to relate variables, which is not possible with other classical methods. In this work, the relationship obtained with the SOM linking M&A language, M&A cross-border, and size of the acquiring company is an important result

Addressing subjectivity in the classification of palaeoenvironmental remains with supervised deep learning convolutional neural networks

Archaeological object identifications have been traditionally undertaken through a comparative methodology where each artefact is identified through a subjective, interpretative act by a professional. Regarding palaeoenvironmental remains, this comparative methodology is given boundaries by using reference materials and codified sets of rules, but subjectivity is nevertheless present. The problem with this traditional archaeological methodology is that higher level of subjectivity in the identification of artefacts leads to inaccuracies, which then increases the potential for Type I and Type II errors in the testing of hypotheses. Reducing the subjectivity of archaeological identifications would improve the statistical power of archaeological analyses, which would subsequently lead to more impactful research. In this thesis, it is shown that the level of subjectivity in palaeoenvironmental research can be reduced by applying deep learning convolutional neural networks within an image recognition framework. The primary aim of the presented research is therefore to further the on-going paradigm shift in archaeology towards model-based object identifications, particularly within the realm of palaeoenvironmental remains. Although this thesis focuses on the identification of pollen grains and animal bones, with the latter being restricted to the astragalus of sheep and goats, there are wider implications for archaeology as these methods can easily be extended beyond pollen and animal remains. The previously published POLEN23E dataset is used as the pilot study of applying deep learning in pollen grain classification. In contrast, an image dataset of modern bones was compiled for the classification of sheep and goat astragali due to a complete lack of available bone image datasets and a double blind study with inexperienced and experienced zooarchaeologists was performed to have a benchmark to which image recognition models can be compared. In both classification tasks, the presented models outperform all previous formal modelling methods and only the best human analysts match the performance of the deep learning model in the sheep and goat astragalus separation task. Throughout the thesis, there is a specific focus on increasing trust in the models through the visualization of the models’ decision making and avenues of improvements to Grad-CAM are explored. This thesis makes an explicit case for the phasing out of the comparative methods in favour of a formal modelling framework within archaeology, especially in palaeoenvironmental object identification

Urban food strategies in Central and Eastern Europe: what's specific and what's at stake?

Integrating a larger set of instruments into Rural Development Programmes implied an increasing focus on monitoring and evaluation. Against the highly diversified experience with regard to implementation of policy instruments the Common Monitoring and Evaluation Framework has been set up by the EU Commission as a strategic and streamlined method of evaluating programmes’ impacts. Its indicator-based approach mainly reflects the concept of a linear, measure-based intervention logic that falls short of the true nature of RDP operation and impact capacity on rural changes. Besides the different phases of the policy process, i.e. policy design, delivery and evaluation, the regional context with its specific set of challenges and opportunities seems critical to the understanding and improvement of programme performance. In particular the role of local actors can hardly be grasped by quantitative indicators alone, but has to be addressed by assessing processes of social innovation. This shift in the evaluation focus underpins the need to take account of regional implementation specificities and processes of social innovation as decisive elements for programme performance.

Abstracts of PhD and MSc theses completed by ILRI graduate fellows 2008

This paper is a compilation of abstracts of PhD and MSc theses by ILRI Graduate Fellows during 2008.The International Livestock Research Institute (ILRI) is one of 15 future harvest centres, which conduct food and environmental research to help alleviate poverty and increase food security while protecting the natural resource base. Building on three decades of experience, ILRI works at the crossroads of livestock and poverty by bringing high quality science and capacity building to bear on poverty reduction and sustainable development. As part of its research-based outreach and capacity strengthening, ILRI assists its partners by offering opportunities for long- and short-term training for researchers and development practitioners. Capacity building is a core priority of ILRI because of the important role it plays in economic growth and development as well as addressing the rapid changes in the bio-physical, socio-cultural, technological and policy environments of the agricultural innovation systems in the developing as well as the developed world. ILRI offers individual and group training courses. Both are aimed at largely building the capacity of the individual. Individual training is focused on harnessing the skills and abilities of individuals to contribute to the realization of developmental goals, which may include improved livestock management systems and enhanced research outputs and outcomes. ILRI has five Categories of individual trainees: Attachment Associates, Student Associates, Technical Associates, Research Fellows and Graduate Fellows. Graduate Fellows are mostly employees undertaking MSc and PhD studies. Postgraduate training is a priority activity of ILRI as it contributes significantly to the research agenda of ILRI and to the national and regional human resource pool in many regions of the world. This group of graduates will continue the research on emerging issues and will form the core of future collaborative research and innovation partners of ILRI. ILRI supports a number of Graduate Fellows every year. The outputs of their work would end up as thesis and various publications. Not every researcher, however, has access to these materials. The result of the research efforts of the Graduate Fellows have limited impact due to lack of wider circulation of their findings. This publication of the abstracts of the thesis work supported by ILRI as a compendium is one way to make sure that the results are widely distributed among the various research and development practitioners working within the livestock innovation system. The purpose is to make the R&D community to be aware of the research work completed and the key findings. If you need additional information, please contact ILRI’s InfoCentre directly where copies of these theses are deposited. We sincerely hope that this document will contribute positively in planning the future research activities and also will eliminate potential duplication of efforts

Modelling Early Food Production in the Mid-Holocene of the Eastern Sahara. A Sustainable Rural Livelihood Approach

The thesis employs an approach adapted from the Sustainable Rural Livelihoods (SRL) model, which was pioneered in development economics. The model provides both descriptive and explanatory components. The purpose of the research is to determine whether the SRL approach can improve the handling of archaeological data and its interpretation. It has been tested in four case studies focusing on early food production in marginal areas of the midHolocene eastern Sahara. It assesses how livelihoods were practiced in terms of risk and sustainability. A strength of the SRL approach is that it incorporates the belief that all aspects of a livelihood should be allocated equal value, including economic, ecological, human wellbeing and social assets. In particular it provides the opportunity to evaluate a qualitative model to improve an understanding of the variables that might have influenced livelihood strategies in prehistory. Ethnographic data has been employed to inform an understanding of the risks and opportunities confronting populations living in arid and semi-arid environments. In the penultimate chapter the thesis compares the findings from the four case studies to test the value of the SRL model for drawing inferences about risk, opportunity and sustainability in arid and semi-arid environments. Whilst the research is not problem-orientated it does identify gaps in current research with a view to recommending new research priorities

The Metabolism of Islands

This book makes the case for why we should care about islands and their sustainability. Islands are hotspots of biocultural diversity and home to 600 million people that depend on one-sixth of the earth’s total area, including the surrounding oceans, for their subsistence. Today, they are at the frontlines of climate change and face an existential crisis. Islands are, however, potential “hubs of innovation” that are uniquely positioned to be leaders in sustainability and climate action. This volume argues that a full-fledged program on “island industrial ecology” is urgently needed, with the aim of offering policy-relevant insights and strategies to sustain small islands in an era of global environmental change. The nine contributions in this volume cover a wide range of applications of socio-metabolic research, from flow accounts to stock analysis and their relationship to services in space and time. They offer insights into how reconfiguring patterns of resource use will allow island governments to build resilience and adapt to the challenges of climate change

Assessment Of Energy Usage Patterns And Improvement Opportunities For Small-Scale Farms In The Western North Carolina Local Food System

This pilot study used a self-reported survey to create energy use profiles (electricity, propane, natural gas, heating oil, diesel, gasoline, and wood) and identify energy use improvement opportunities for small farms participating in local markets in western North Carolina. Higher proportions of energy use across all study farms, as compared to the centralized agricultural system, came from gasoline and electricity, with high variability in energy usage mixes between and within farm types. Opportunities for on-farm energy improvement (mostly in tractor use and irrigation) and system-wide energy improvement (mostly in transportation and storage) were identified. Solar energy resources were available on 94% of study farms, and micro-hydro and passive pump developments were possible for an estimated 40% of farms. However, only 7-10% of the farms were eligible for USDA energy grants. Energy efficiencies per unit of land (in GJ/ha) for each farm type were compared with those observed in national agricultural statistics, but the limited data about indirect energy usage and agricultural output per hectare suggested further study is needed to better understand similarities and differences. Ultimately, a more focused survey with clarifying follow-up phone interviews could provide a more thorough portrayal of small farm energy usage, needs, and improvement opportunities

Spreading the Washington Consensus into Food and Agriculture Sectors: The Case of the International Monetary Fund

The mandate and competence of the International Monetary Fund (IMF) do not cover food and agriculture policies. Yet, signs indicate that IMF enages in these policies. Scholars lack a systematic empirical foundation to monitor the extent and impact of IMF’s operations on these sectors. Based on a combination of machine and human coding, we present a comprehensive database on IMF’s policy interventions in food and agriculture. Using new data on IMF conditionality between 1980 and 2014, we assess to what extent the IMF targets these sectors through its ‘conditionalities’—policies that governments need to implement to access IMF credit. The analysis evaluates the agricultural content and ideological orientation of each condition according to whether it promotes a developmental state, a night-watchman state, or neither. The analysis identifies that about 2% of all IMF conditions (1,105 of 58,406) directly target food and agriculture issues. These conditions are available in 43% of all IMF programs (332 of 781). They affect 100 countries of all the 131 countries in which the IMF had any agreements since the 1980s. In addition, the analysis reveals that 59.2% of these conditions embody policy measures in line with a night-watchman state, 40.1% are model-neutral, and 0.7% are developmental. Within the model-neutral category, 23.9% are conditions oriented towards building state capacity; 2.7% have a poverty reduction content; and 2.9% contain pro- environmental policies. The article discusses potential mechanisms driving the IMF to intervene into agriculture and theorizes about possible effects of these conditions on people’s livelihoods