Testing Modeling Assumptions in the West Africa Ebola Outbreak

The Ebola virus in West Africa has infected almost 30,000 and killed over 11,000 people. Recent models of Ebola Virus Disease (EVD) have often made assumptions about how the disease spreads, such as uniform transmissibility and homogeneous mixing within a population. In this paper, we test whether these assumptions are necessarily correct, and offer simple solutions that may improve disease model accuracy. First, we use data and models of West African migration to show that EVD does not homogeneously mix, but spreads in a predictable manner. Next, we estimate the initial growth rate of EVD within country administrative divisions and find that it significantly decreases with population density. Finally, we test whether EVD strains have uniform transmissibility through a novel statistical test, and find that certain strains appear more often than expected by chance.Comment: 16 pages, 14 figure

¿Convergen las diferentes disciplinas de conocimiento? evidencia cuantitativa

Traditional epistemological models classify knowledge into separate disciplines with different objects of study and specific techniques, with some frameworks even proposing hierarchies (such as Comte’s). According to thinkers such as John Holland or Teilhard de Chardin, the advancement of science involves the convergence of disciplines. This proposed convergence can be studied in a number of ways, such as how works impact research outside a specific area (citation networks) or how authors collaborate with other researchers in different fields (collaboration networks). While these studies are delivering significant new insights, they cannot easily show the convergence of different topics within a body of knowledge. This paper attempts to address this question in a quantitative manner, searching for evidence that supports the idea of convergence in the content of the sciences themselves (that is, whether the sciences are dealing with increasingly the same topics). We use Latent Dirichlet Analysis (LDA), a technique that is able to analyze texts and estimate the relative contributions of the topics that were used to generate them. We apply this tool to the corpus of the Santa Fe Institute (SFI) working papers, which spans research on Complexity Science from 1989 to 2015. We then analyze the relatedness of the different research areas, the rise and demise of these sub-disciplines over time and, more broadly, the convergence of the research body as a whole. Combining the topic structure obtained from the collected publication history of the SFI community with techniques to infer hierarchy and clustering, we reconstruct a picture of a dynamic community which experiences trends, periodically recurring topics, and shifts in the closeness of scholarship over time. We find that there is support for convergence, and that the application of quantitative methods such as LDA to the study of knowledge can provide valuable insights that can help researchers navigate the increasingly wide literature as well as identifying potentially fruitful areas for research collaboration.Los modelos epistemológicos tradicionales clasifican el conocimiento en disciplinas separadas con objetos de estudio distintos y técnicas específicas, incluso proponiendo esquemas jerárquicos (por ejemplo, Comte). Según pensadores como John Holland o Teilhard de Chardin, el avance de la ciencia implica una convergencia entre sus disciplinas. Esta convergencia puede estudiarse de maneras distintas, como el impacto de diferentes autores fuera de su equipo (redes de citación) o la manera en la que colaboran (redes de coautoría). Aunque estos estudios están generando ideas interesantes, no son capaces de mostrar la convergencia de los distintos temas que se tratan en un cuerpo de trabajos. Este artículo intenta estudiar esta pregunta desde un punto de vista cuantitativo, buscando evidencias que apoyen la idea de convergencia en el contenido de las ciencias en sí mismas (es decir, si las ciencias se ocupan de temas cada vez más cercanos entre ellos). Empleamos Latent Dirichlet Analysis (LDA), una técnica que analiza textos y estima las contribuciones relativas de los temas que los generan (estos temas se definen como distribuciones de palabras). Aplicamos esta técnica al corpus de artículos publicados por el Instituto de Santa Fe (Santa Fe Institute, SFI), que describe trabajos relacionados con las Ciencias de la Complejidad entre 1989 y 2015. Analizamos la cercanía entre las diferentes áreas, la aparición y desaparición de temas de investigación y, en general, la posible convergencia entre disciplinas. Combinando la estructura obtenida de la historia de las publicaciones de SFI con técnicas de inferencia de jerarquía y clustering, reconstruimos la perspectiva de una comunidad científica dinámica que experimenta tendencias, temas recurrentes y cambios en la cercanía de las diferentes disciplinas. Nuestros resultados muestran que hay evidencias de convergencia y que la aplicación de métodos cuantitativos puede proporcionar nuevos elementos de comprensión que ayuden a los investigadores a estructurar una literatura científica cada vez más amplia y compleja, así como a identificar áreas potenciales para nuevas colaboraciones

¿Convergen las diferentes disciplinas de conocimiento? Evidencia cuantitativa

Artículos en revistasinfo:eu-repo/semantics/publishedVersio

Quantifying convergence in the sciences

Traditional epistemological models classify knowledge into separate disciplines with different objects of study and specific techniques, with some frameworks even proposing hierarchies (such as Comte s). According to thinkers such as John Holland or Teilhard de Chardin, the advancement of science involves the convergence of disciplines. This proposed convergence can be studied in a number of ways, such as how works impact research outside a specific area (citation networks) or how authors collaborate with other researchers in different fields (collaboration networks). While these studies are delivering significant new insights, they cannot easily show the convergence of different topics within a body of knowledge. This paper attempts to address this question in a quantitative manner, searching for evidence that supports the idea of convergence in the content of the sciences themselves (that is, whether the sciences are dealing with increasingly the same topics). We use Latent Dirichlet Analysis (LDA), a technique that is able to analyze texts and estimate the relative contributions of the topics that were used to generate them. We apply this tool to the corpus of the Santa Fe Institute (SFI) working papers, which spans research on Complexity Science from 1989 to 2015. We then analyze the relatedness of the different research areas, the rise and demise of these sub?disciplines over time and, more broadly, the convergence of the research body as a whole. Combining the topic structure obtained from the collected publication history of the SFI community with techniques to infer hierarchy and clustering, we reconstruct a picture of a dynamic community which experiences trends, periodically recurring topics, and shifts in the closeness of scholarship over time. We find that there is support for convergence, and that the application of quantitative methods such as LDA to the study of knowledge can provide valuable insights that can help researchers navigate the increasingly wide literature as well as identifying potentially fruitful areas for research collaboration.info:eu-repo/semantics/draf

Quantifying convergence in the sciences

Traditional epistemological models classify knowledge into separate disciplines with different objects of study and specific techniques, with some frameworks even proposing hierarchies (such as Comte’s). According to thinkers such as John Holland or Teilhard de Chardin, the advancement of science involves the convergence of disciplines. This proposed convergence can be studied in a number of ways, such as how works impact research outside a specific area (citation networks) or how authors collaborate with other researchers in different fields (collaboration networks). While these studies are delivering significant new insights, they cannot easily show the convergence of different topics within a body of knowledge. This paper attempts to address this question in a quantitative manner, searching for evidence that supports the idea of convergence in the content of the sciences themselves (that is, whether the sciences are dealing with increasingly the same topics). We use Latent Dirichlet Analysis (LDA), a technique that is able to analyze texts and estimate the relative contributions of the topics that were used to generate them. We apply this tool to the corpus of the Santa Fe Institute (SFI) working papers, which spans research on Complexity Science from 1989 to 2015. We then analyze the relatedness of the different research areas, the rise and demise of these sub-disciplines over time and, more broadly, the convergence of the research body as a whole. Combining the topic structure obtained from the collected publication history of the SFI community with techniques to infer hierarchy and clustering, we reconstruct a picture of a dynamic community which experiences trends, periodically recurring topics, and shifts in the closeness of scholarship over time. We find that there is support for convergence, and that the application of quantitative methods such as LDA to the study of knowledge can provide valuable insights that can help researchers navigate the increasingly wide literature as well as identifying potentially fruitful areas for research collaboration

Quantifying convergence in the sciences

Artículos en revistasTraditional epistemological models classify knowledge into separate disciplines with different objects of study and specific techniques, with some frameworks even proposing hierarchies (such as Comte s). According to thinkers such as John Holland or Teilhard de Chardin, the advancement of science involves the convergence of disciplines. This proposed convergence can be studied in a number of ways, such as how works impact research outside a specific area (citation networks) or how authors collaborate with other researchers in different fields (collaboration networks). While these studies are delivering significant new insights, they cannot easily show the convergence of different topics within a body of knowledge. This paper attempts to address this question in a quantitative manner, searching for evidence that supports the idea of convergence in the content of the sciences themselves (that is, whether the sciences are dealing with increasingly the same topics). We use Latent Dirichlet Analysis (LDA), a technique that is able to analyze texts and estimate the relative contributions of the topics that were used to generate them. We apply this tool to the corpus of the Santa Fe Institute (SFI) working papers, which spans research on Complexity Science from 1989 to 2015. We then analyze the relatedness of the different research areas, the rise and demise of these sub?disciplines over time and, more broadly, the convergence of the research body as a whole. Combining the topic structure obtained from the collected publication history of the SFI community with techniques to infer hierarchy and clustering, we reconstruct a picture of a dynamic community which experiences trends, periodically recurring topics, and shifts in the closeness of scholarship over time. We find that there is support for convergence, and that the application of quantitative methods such as LDA to the study of knowledge can provide valuable insights that can help researchers navigate the increasingly wide literature as well as identifying potentially fruitful areas for research collaboration.info:eu-repo/semantics/publishedVersio

2-Aminoadipic acid is a marker of protein carbonyl oxidation in the aging human skin: effects of diabetes, renal failure and sepsis

We hypothesized that the ϵ-amino group of lysine residues in longlived proteins oxidatively deaminates with age forming the carbonyl compound, allysine (α-aminoadipic acid-δ-semialdehyde), which can further oxidize into 2-aminoadipic acid. In the present study, we measured both products in insoluble human skin collagen from n=117 individuals of age range 10–90 years, of which n=61 and n=56 were non-diabetic and diabetic respectively, and a total of n=61 individuals had either acute or chronic renal failure. Allysine was reduced by borohydride into 6-hydroxynorleucine and both products were measured in acid hydrolysates by selective ion monitoring gas chromatography (GC)-MS. The results showed that 2-aminoadipic acid (P<0.0001), but not 6-hydroxynorleucine (P=0.14), significantly increased with age reaching levels of 1 and 0.3 mmol/mol lysine at late age respectively. Diabetes in the absence of renal failure significantly (P<0.0001) increased 2-aminoadipic acid up to <3 mmol/mol, but not 6-hydroxynorleucine (levels<0.4 mmol/mol, P=0.18). Renal failure even in the absence of diabetes markedly increased levels reaching up to <0.5 and 8 mmol/mol for 6-hydroxynorleucine and 2-aminoadipic acid respectively. Septicaemia significantly (P<0.0001) elevated 2-aminoadipic acid in non-diabetic, but not diabetic individuals, and mildly correlated with other glycoxidation markers, carboxymethyl-lysine and the methylglyoxal-derived products, carboxyethyl-lysine, argpyrimidine and MODIC (methylglyoxal-derived imidazolium cross-link). These results provide support for the presence of metal-catalysed oxidation (the Suyama pathway) in diabetes and the possible activation of myeloperoxidase during sepsis. We conclude that 2-aminoadipic acid is a more reliable marker for protein oxidation than its precursor, allysine. Its mechanism of formation in each of these conditions needs to be elucidated