Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 199

This bibliography lists 82 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1979

Graduate School: Course Decriptions, 1972-73

Official publication of Cornell University V.64 1972/7

An automated pattern recognition system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies

This paper presents an automated system for the quantification of inflammatory cells in hepatitis-C-infected liver biopsies. Initially, features are extracted from colour-corrected biopsy images at positions of interest identified by adaptive thresholding and clump decomposition. A sequential floating search method and principal component analysis are used to reduce dimensionality. Manually annotated training images allow supervised training. The performance of Gaussian parametric and mixture models is compared when used to classify regions as either inflammatory or healthy. The system is optimized using a response surface method that maximises the area under the receiver operating characteristic curve. This system is then tested on images previously ranked by a number of observers with varying levels of expertise. These results are compared to the automated system using Spearman rank correlation. Results show that this system can rank 15 test images, with varying degrees of inflammation, in strong agreement with five expert pathologists

Proceedings of the 2nd Annual Conference on NASA/University Advanced Space Design Program

Topics discussed include: lunar transportation system, Mars rover, lunar fiberglass production, geosynchronous space stations, regenerative system for growing plants, lunar mining devices, lunar oxygen transporation system, mobile remote manipulator system, Mars exploration, launch/landing facility for a lunar base, and multi-megawatt nuclear power system

A machine learning taxonomic classifier for science publications

Dissertação de mestrado integrado em Engineering and Management of Information SystemsThe evolution in scientific production, associated with the growing interdomain collaboration of knowledge and the increasing co-authorship of scientific works remains supported by processes of manual, highly subjective classification, subject to misinterpretation. The very taxonomy on which this same classification process is based is not consensual, with governmental organizations resorting to taxonomies that do not keep up with changes in scientific areas, and indexers / repositories that seek to keep up with those changes. We find a reality distinct from what is expected and that the domains where scientific work is recorded can easily be misrepresentative of the work itself. The taxonomy applied today by governmental bodies, such as the one that regulates scientific production in Portugal, is not enough, is limiting, and promotes classification in areas close to the desired, therefore with great potential for error. An automatic classification process based on machine learning algorithms presents itself as a possible solution to the subjectivity problem in classification, and while it does not solve the issue of taxonomy mismatch this work shows this possibility with proved results. In this work, we propose a classification taxonomy, as well as we develop a process based on machine learning algorithms to solve the classification problem. We also present a set of directions for future work for an increasingly representative classification of evolution in science, which is not intended as airtight, but flexible and perhaps increasingly based on phenomena and not just disciplines.A evolução na produção de ciência, associada à crescente colaboração interdomínios do conhecimento e à também crescente coautoria de trabalhos permanece suportada por processos de classificação manual, subjetiva e sujeita a interpretações erradas. A própria taxonomia na qual assenta esse mesmo processo de classificação não é consensual, com organismos estatais a recorrerem a taxonomias que não acompanham as alterações nas áreas científicas, e indexadores/repositórios que procuram acompanhar essas mesmas alterações. Verificamos uma realidade distinta do espectável e que os domínios onde são registados os trabalhos científicos podem facilmente estar desenquadrados. A taxonomia hoje aplicada pelos organismos governamentais, como o caso do organismo que regulamenta a produção científica em Portugal, não é suficiente, é limitadora, e promove a classificação em domínios aproximados do desejado, logo com grande potencial para erro. Um processo de classificação automática com base em algoritmos de machine learning apresenta-se como uma possível solução para o problema da subjetividade na classificação, e embora não resolva a questão do desenquadramento da taxonomia utilizada, é apresentada neste trabalho como uma possibilidade comprovada. Neste trabalho propomos uma taxonomia de classificação, bem como nós desenvolvemos um processo baseado em machine learning algoritmos para resolver o problema de classificação. Apresentamos ainda um conjunto de direções para trabalhos futuros para uma classificação cada vez mais representativa da evolução nas ciências, que não pretende ser hermética, mas flexível e talvez cada vez mais baseada em fenómenos e não apenas em disciplinas

One Health – an Ecological and Evolutionary Framework for tackling Neglected Zoonotic Diseases

Understanding the complex population biology and transmission ecology of multihost parasites has been declared as one of the major challenges of biomedical sciences for the 21st century and the Neglected Zoonotic Diseases (NZDs) are perhaps the most neglected of all the Neglected Tropical Diseases (NTDs). Here we consider how multihost parasite transmission and evolutionary dynamics may affect the success of human and animal disease control programmes, particularly neglected diseases of the developing world. We review the different types of zoonotic interactions that occur, both ecological and evolutionary, their potential relevance for current human control activities, and make suggestions for the development of an empirical evidence base and theoretical framework to better understand and predict the outcome of such interactions. In particular, we consider whether preventive chemotherapy, the current mainstay of NTD control, can be successful without a One Health approach. Transmission within and between animal reservoirs and humans can have important ecological and evolutionary consequences, driving the evolution and establishment of drug resistance, as well as providing selective pressures for spill‐over, host switching, hybridizations and introgressions between animal and human parasites. Our aim here is to highlight the importance of both elucidating disease ecology, including identifying key hosts and tailoring control effort accordingly, and understanding parasite evolution, such as precisely how infectious agents may respond and adapt to anthropogenic change. Both elements are essential if we are to alleviate disease risks from NZDs in humans, domestic animals and wildlife