Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

Life Cycle Assessment in the Olive Oil sector

The olive oil industry is a significant productive sector in the European Union and the related production process is characterised by a variety of different practices and techniques for the agricultural production of olives and for their processing into olive oil. Depending on these different procedures, olive oil production is associated with several adverse effects on the environment, both in the agricultural and in the olive oil production phase. As a consequence, tools such as LCA are becoming increasingly important for this type of industry. Following an overview of the characteristics of the olive oil supply chain and its main environmental problems, the authors of this chapter provide a description of the international state of the art of LCA implementation in this specific sector, as well as briefly describing other life cycle thinking methodologies and tools (such as simplified LCA, footprint labels and Environmental Product Declarations). Then, the methodological problems connected with the application of LCA in the olive oil production sector are analysed in depth, starting from a critical comparative analysis of the applicative LCA case studies in the olive oil production supply chain. Finally, guidelines for the application of LCA in the olive oil production sector are proposed

An Efficient Framework of Utilizing the Latent Semantic Analysis in Text Extraction

The use of the latent semantic analysis (LSA) in text mining demands large space and time requirements. This paper proposes a new text extraction method that sets a framework on how to employ the statistical semantic analysis in the text extraction in an efficient way. The method uses the centrality feature and omits the segments of the text that have a high verbatim, statistical, or semantic similarity with previously processed segments. The identification of similarity is based on a new multi-layer similarity method that computes the similarity in three statistical layers, it uses the Jaccard similarity and the vector space model in the first and second layers respectively, and uses the LSA in the third layer. The multi-layer similarity restricts the use of the third layer for the segments that the first and second layers failed to estimate their similarities. Rouge tool is used in the evaluation, but because Rouge does not consider the extract’s size, we supplemented it with a new evaluation strategy based on the compression rate and the ratio of the sentences intersections between the automatic and the reference extracts. Our comparisons with classical LSA and traditional statistical extractions showed that we reduced the use of the LSA procedure by 52%, and we obtained 65% reduction on the original matrix dimensions, also, we obtained remarkable accuracy results. It is concluded that the employment of the centrality feature with the proposed multi-layer framework yields a significant solution in terms of efficiency and accuracy in the field of text extraction