Abstractions Based on Circles

Stan Beckensall is renowned for his work, done on an entirely amateur basis, discovering, recording and interpreting Atlantic rock art in his home county of Northumberland and beyond. Presented on his 90th birthday, this diverse and stimulating collection of papers celebrates his crucial contribution to rock art studies, and looks to the future

Complete 2020 Program

Program and schedule of events for the 31st Annual John Wesley Powell Student Research Conference

Abstractions Based on Circles

Stan Beckensall is renowned for his work, done on an entirely amateur basis, discovering, recording and interpreting Atlantic rock art in his home county of Northumberland and beyond. Presented on his 90th birthday, this diverse and stimulating collection of papers celebrates his crucial contribution to rock art studies, and looks to the future

イメージング質量分析を用いた分子組織学的解析 : Klotho欠損マウスの腎および骨において

広島大学(Hiroshima University)博士(歯学)Doctor of Philosophy in Dental Sciencedoctora

Attentive monitoring of multiple video streams driven by a Bayesian foraging strategy

In this paper we shall consider the problem of deploying attention to subsets of the video streams for collating the most relevant data and information of interest related to a given task. We formalize this monitoring problem as a foraging problem. We propose a probabilistic framework to model observer's attentive behavior as the behavior of a forager. The forager, moment to moment, focuses its attention on the most informative stream/camera, detects interesting objects or activities, or switches to a more profitable stream. The approach proposed here is suitable to be exploited for multi-stream video summarization. Meanwhile, it can serve as a preliminary step for more sophisticated video surveillance, e.g. activity and behavior analysis. Experimental results achieved on the UCR Videoweb Activities Dataset, a publicly available dataset, are presented to illustrate the utility of the proposed technique.Comment: Accepted to IEEE Transactions on Image Processin

A Novel Platform for Multiplexed N-glycoprotein Biomarker Discovery for Hepatocellular Carcinoma by Antibody Panel Based N-glycan Imaging

The vast majority of biomarkers used in the detection of cancer are glycoproteins, and numerous studies have indicated that the N-glycosylation of serum glycoproteins changes with the development of hepatocellular carcinoma (HCC). However, current biomarkers for HCC are lacking in sensitivity and specificity, and there is a need for higher throughput techniques to discover more powerful biomarkers. The majority of methods that do analyze N-glycans and their protein carriers generally require large amounts of sample preparation and/or look at only one protein at a time, which is a barrier for translating discoveries to the clinic. In response to this need for multiplexed biomarker analysis of protein-specific N-glycan changes, we developed a novel platform for the simultaneous analysis of potentially 100s of N-linked glycoproteins from biofluids with the goal of discovering new clinically-relevant cancer biomarkers. This new mass spectrometry imaging platform for multiplexed N-glycoprotein biomarker was applied to multiple cohorts of cirrhotic and HCC patient serum samples. An antibody panel encompassing antibodies for seven glycoproteins was used in the analysis of two cohorts consisting of 100 patients. These data were used to create biomarker algorithms incorporating protein-specific glycan signatures and clinical information. These models produced AUROCs of 0.9289 and 0.9278 for differentiating HCC from cirrhosis, which were significant improvements on the currently used biomarker AFP. We also expect that this platform can be expanded for biomarker discovery of other types of cancers and diseases from numerous types of biofluids

Musculoskeletal Load Exposure Estimation by Non-supervised Annotation of Events on Motion Data

There is a significant number of work pressures that promote the incidence of musculoskeletal disorders in industrial environments. As, unfortunately, many workplace conditions are subject to these biomechanical hazards, this has become an extensively common health disorder. To properly adjust intervention strategies, an ergonomic assessment through surveillance measurements is required. However, most measurements still depend on subjective assessment tools like self-reporting and expert observation. The ideal approach for this scenario would be to use direct measurements that use sensors to retrieve more precise/accurate information of how workers interact with their work environment. Following this approach, one of the major constraints would be that a systematic retrieval of data from a labor environment would require a tiresome process of analysis and manual annotation, deviating resources and requiring data analysts. Hence, this work proposes an unsupervised methodology able to automatically annotate relevant events from direct acquisitions, with the final intent of promoting this type of analysis. The event detection methodology proposes to detect three different event types: 1) work period transition; 2) work cycle transition; and 3) sub-sequence matching by query. To achieve this, the multivariate time series are represented as a Self-Similarity matrix built with the features extracted. This matrix is analysed for each event needed to be searched. The results were successful in the segmentation of Active and Non-active working periods and in the detection of points of transition between repetitive human motions, i.e. work cycles. A method of search-by-example is also presented, being that it allows for the user to detect specific motions of interest. Although this method could still be further optimized in future work, this approach has a very promising prospect as it proposes a strategy of similarity analysis that has not yet been deeply explored in the context of ergonomic acquisition. These advances are also significant given that the summarization of ergonomic data is still a subject in expansion.Num contexto industrial, são várias as tensões que promovem a incidência de distúrbios musculosqueléticos. Uma vez que a maioria das condições laborais estão sujeitas a estas propensões do foro biomecânico, os distúrbiosmusculosqueléticos tornaram-se patologias amplamente diagnosticadas na população ativa. Para desenhar estratégias de intervenção eficientes, é necessário proceder a uma avaliação ergonómica baseada em metododologias de vigilância. Não obstante o reconhecimento desta necessidade, a maioria das medidas ainda depende de ferramentas subjetivas como a auto-avaliação e a observação externa por parte de especialistas. A abordagem preferencial para esta problemática passaria pela aplicação de medições diretas que recorressem a sensores com vista a extrair informação exata e fidedigna do ambiente laboral. Uma das maiores limitações deste leque de soluções consiste no facto de um sistema de recolha de dados neste ambiente implicar um processo exaustivo de análise e anotação manual, o que consome recursos e requer os serviços de analistas de dados. Assim, este trabalho propõe uma metodologia capaz de anotar automaticamente eventos relevantes provenientes de aquisições diretas, com o objetivo final de promover este tipo de análises mais eficientes. A metodologia de deteção de eventos proposta foca-se em três diferentes tipos de eventos: 1) transições entre tarefas; 2) transições entre ciclos de trabalho; e 3) procura de movimentos-exemplo em amostras segmentadas. Para concretizar este trabalho, realizou-se um estudo de matrizes de auto-semelhança. Os resultados provaram-se, na sua maioria, bem-sucedidos no caso da segmentação de períodos Ativos e Não-ativos e na deteção de momentos de transição entre movimentos repetitivos, isto é, ciclos de trabalho. É ainda apresentado um método de procura-porexemplo que permite ao utilizador detetar movimentos-exemplo do seu interesse. Embora este método possa ainda ser otimizado em trabalhos futuros, reflete uma abordagem promissora uma vez que propõe uma estratégia de análise de similaridade que não foi ainda especialmente explorada no contexto dos estudos ergonómicos. Estes avanços são ainda significantes na perspetiva de que a sumarização de dados ergonómicos é uma linha de investigação ainda em expansão

Treasures of Time. Research of the Faculty of Archaeology of Adam Mickiewicz University in Poznań

This publication presents the current scientific interests creatively developed by such teams at the Faculty of Archaeology of Adam Mickiewicz University. The research of these teams covers vast areas in time and space, summing up at least the last 9,000 years of prehistory. The following articles, arranged in chronological order, allow us to explore the prehistory of various areas