Interactive visualization for information analysis in medical diagnosis

This paper investigates to what extend the findings and solutions of information analysis in intelligence analysis can be applied and transferred into the medical diagnosis domains. Interactive visualization is proposed to address some of the problems faced by both domain. Its design issues related to selected common problems are then discussed in details. Finally, a visual sense making system INVISQUE is used as an example to illustrate how the interactive visualization can be used to support information analysis and medical diagnosis

Mechanisms of biliary carcinogenesis: A pathogenetic multi-stage cascade towards cholangiocarcinoma

Carcinomas of the biliary tract are rare cancers developing from the epithelial or blast-like cells lining the bile ducts. A variety of known predisposing factors and recent experimental modelsof biliary carcinogenesis (e.g., infection with the liver fluke Opisthorchis viverrini, models of chemically induced carcinogenesis and experimental models of pancreaticobiliary maljunction) have elucidated different stages of this complex system of biliary tumorigenesis. Chronic inflammatory processes, generation of active oxygen radicals, altered cellular detoxificationmechanisms, activation of oncogenes, functional loss of tumor-suppressor genes and dysregulation of cell proliferation and cell apoptotic mechanisms have been identified as important contributorsin the development of cholangiocarcinomas. In this review, the known mechanisms involved in the carcinogenesis of biliary epithelium are addressed. We will divide the topic into four stages: 1) Predisposition and risk factors of biliary cancer. 2) Genotoxic events and alterations leading to specific DNA damage and mutation patterns. 3) Dysregulation of DNA repair mechanisms and apoptosis, permitting survival of mutated cells and 4) Morphological evolution from premalignant biliary lesions to cholangiocarcinoma. Finally, established and hypothetical future therapeutic strategies directed towards specific pathogenetic events during biliary carcinogenesis will be addresse

The Trials and Tribulations of Applied Triangulation: Weighing Different Data Sources

Researchers employ triangulation to increase the validity of inference in qualitative and quantitative research. Leuffen, Shikano, and Walter have presented guidance as to which strategies to use when triangulating data sources. In this article, I explore how their findings can be translated for practical research purposes. I offer an illustrative application concentrating on the political power of traditional political authorities in Uganda and Tanzania. I analyze the status quo of political power and the preferred political power of traditional leaders. To triangulate, I use three sources: (1) constitutional-legal texts, (2) the Afrobarometer survey, and (3) in-depth interviews. I shed light on possible problems and analytical strategies for triangulation in practice, with a specific focus on convergence and divergence of sources

Explainable AI: The new 42?

Explainable AI is not a new field. Since at least the early exploitation of C.S. Pierce’s abductive reasoning in expert systems of the 1980s, there were reasoning architectures to support an explanation function for complex AI systems, including applications in medical diagnosis, complex multi-component design, and reasoning about the real world. So explainability is at least as old as early AI, and a natural consequence of the design of AI systems. While early expert systems consisted of handcrafted knowledge bases that enabled reasoning over narrowly well-defined domains (e.g., INTERNIST, MYCIN), such systems had no learning capabilities and had only primitive uncertainty handling. But the evolution of formal reasoning architectures to incorporate principled probabilistic reasoning helped address the capture and use of uncertain knowledge. There has been recent and relatively rapid success of AI/machine learning solutions arises from neural network architectures. A new generation of neural methods now scale to exploit the practical applicability of statistical and algebraic learning approaches in arbitrarily high dimensional spaces. But despite their huge successes, largely in problems which can be cast as classification problems, their effectiveness is still limited by their un-debuggability, and their inability to “explain” their decisions in a human understandable and reconstructable way. So while AlphaGo or DeepStack can crush the best humans at Go or Poker, neither program has any internal model of its task; its representations defy interpretation by humans, there is no mechanism to explain their actions and behaviour, and furthermore, there is no obvious instructional value.. the high performance systems can not help humans improve. Even when we understand the underlying mathematical scaffolding of current machine learning architectures, it is often impossible to get insight into the internal working of the models; we need explicit modeling and reasoning tools to explain how and why a result was achieved. We also know that a significant challenge for future AI is contextual adaptation, i.e., systems that incrementally help to construct explanatory models for solving real-world problems. Here it would be beneficial not to exclude human expertise, but to augment human intelligence with artificial intelligence

Observations of oxidation products above a forest imply biogenic emissions of very reactive compounds

International audienceVertical gradients of mixing ratios of volatile organic compounds have been measured in a Ponderosa pine forest in Central California (38.90° N, 120.63° W, 1315m). These measurements reveal large quantities of previously unreported oxidation products of short lived biogenic precursors. The emission of biogenic precursors must be in the range of 13-66µmol m-2h-1 to produce the observed oxidation products. That is 6-30 times the emissions of total monoterpenes observed above the forest canopy on a molar basis. These reactive precursors constitute a large fraction of biogenic emissions at this site, and are not included in current emission inventories. When oxidized by ozone they should efficiently produce secondary aerosol and hydroxyl radicals

Observations of oxidation products above a forest imply biogenic emissions of very reactive compounds

International audienceMeasurements of volatile organic compounds in a pine forest (Central California, 38.90° N, 120.63° W, 1315 m) reveal large quantities of previously unreported oxidation products of short lived biogenic precursors. The emission of biogenic precursors must be in the range of 13?66 µmol m?2 h?1 to produce the observed oxidation products. That is 6?30 times the emissions of total monoterpenes observed above the forest canopy on a molar basis. These reactive precursors constitute the largest fraction of biogenic emissions at this site, and are not included in current emission inventories. When oxidized by ozone they should efficiently produce secondary aerosol and hydroxyl radicals

Cell wall characteristics during sexual reproduction of Mougeotia sp. (Zygnematophyceae) revealed by electron microscopy, glycan microarrays and RAMAN spectroscopy

Mougeotia spp. collected from field samples were investigated for their conjugation morphology by light-, fluorescence-, scanning- and transmission electron microscopy. During a scalarifom conjugation, the extragametangial zygospores were initially surrounded by a thin cell wall that developed into a multi-layered zygospore wall. Maturing zygospores turned dark brown and were filled with storage compounds such as lipids and starch. While M. parvula had a smooth surface, M. disjuncta had a punctated surface structure and a prominent suture. The zygospore wall consisted of a polysaccharide rich endospore, followed by a thin layer with a lipid-like appaerance, a massive electron dense mesospore and a very thin exospore composed of polysaccharides. Glycan microarray analysis of zygospores of different developmental stages revealed the occurrence of pectins and hemicelluloses, mostly composed of homogalacturonan (HG), xyloglucans, xylans, arabino-galactan proteins and extensins. In situ localization by the probe OG7-13AF 488 labelled HG in young zygospore walls, vegetative filaments and most prominently in conjugation tubes and cross walls. Raman imaging showed the distribution of proteins, lipids, carbohydrates and aromatic components of the mature zygospore with a spatial resolution of ~ 250 nm. The carbohydrate nature of the endo- and exospore was confirmed and in-between an enrichment of lipids and aromatic components, probably algaenan or a sporopollenin-like material. Taken together, these results indicate that during zygospore formation, reorganizations of the cell walls occured, leading to a resistant and protective structure

Impact of Entity Graphs on Extracting Semantic Relations

International audienceRelation extraction (RE) between a pair of entity mentions from text is an important and challenging task specially for open domain relations. Generally, relations are extracted based on the lexical and syntactical information at the sentence level. However, global information about known entities has not been explored yet for RE task. In this paper, we propose to extract a graph of entities from the overall corpus and to compute features on this graph that are able to capture some evidences of holding relationships between a pair of entities. The proposed features boost the RE performance significantly when these are combined with some linguistic features