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| 1 | Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery显示文摘 | Renaud Mathieu Claire Freeman Jagannath Aryal | 2006 | Landscape and Urban Planning2006，，3： | 1 |
| 2 | Assessing the applicability of the V–I–S model to map urban land use in the developing world: Case study of Yogyakarta, Indonesia显示文摘 | Hery Setiawan Renaud Mathieu Michelle Thompson-Fawcett | 2005 | Computers, Environment and Urban Systems2005，，4： | 1 |
| 3 | Map- ping private gardens in urban areas using object -oriented techniques and very high - resolution satellite imagery 显示文摘 | Renaud Mathieu Claire Freeman and Jagannath Aryal | 2007 | Landscape and Urban Planning2007，81，3： | 1 |
| 4 | Fusion of spatiotemporal and thematic features of textual data for animal disease surveillance显示文摘Several internet-based surveillance systems have been created to monitor the web for animal health surveillance.These systems collect a large amount of news dealing with outbreaks related to animal diseases.Automatically identifying news articles that describe the same outbreak event is a key step to quickly detect relevant epidemiological information while alleviating manual curation of news content.This paper addresses the task of retrieving news articles that are related in epidemiological terms.We tackle this issue using text mining and feature fusion methods.The main objective of this paper is to identify a textual representation in which two articles that share the same epidemiological content are close.We compared two types of representations(i.e.,features)to represent the documents:(i)morphosyntactic features(i.e.,selection and transformation of all terms from the news,based on classical textual processing steps)and(ii)lexicosemantic features(i.e.,selection,transformation and fusion of epidemiological terms including diseases,hosts,locations and dates).We compared two types of term weighing(i.e.,Boolean and TF-IDF)for both representations.To combine and transform lexicosemantic features,we compared two data fusion techniques(i.e.,early fusion and late fusion)and the effect of features generalisation,while evaluating the relative importance of each type of feature.We conducted our analysis using a corpus composed of a subset of news articles in English related to animal disease outbreaks.Our results showed that the combination of relevant lexicosemantic(epidemiological)features using fusion methods improves classical morphosyntactic representation in the context of disease-related news retrieval.The lexicosemantic representation based on TF-IDF and feature generalisation(F-measure=0.92,r-precision=0.58)outperformed the morphosyntactic representation(F-measure=0.89,r-precision=0.45),while reducing the features space.Converting the features into lower granular features(i.e.,generalisation)contributed to improving the results of the lexicosemantic representation.Our results showed no difference between the early and late fusion approaches.Temporal features performed poorly on their own.Conversely,spatial features were the most discriminative features,highlighting the need for robust methods for spatial entity extraction,disambiguation and representation in internet-based surveillance systems. | Sarah Valentin Renaud Lancelot Mathieu Roche | 2023 | Information Processing in Agriculture2023，10，3： | 0 |
| 5 | MP联合沙利度胺、MP和低强度自体干细胞移植治疗老年多发性骨髓瘤(IFM99—06):一项随机试验显示文摘背景对于老年人多发性骨髓瘤,马法兰与泼尼松联合化疗方案(MP)目前仍被认为是标准治疗方案。本试验旨在评价在MP方案的基础上联用沙利度胺或低强度的自体干细胞移植能否提高患者的存活率。
方法从2000年5月22日到2005年8月8日,共招募447例初治多发性骨髓瘤患者,年龄65~75岁,随机接受MP方案(n=196)、MP联合沙利度胺方案(MPT,n=125)或低强度(马法兰,100mg/m^2)的自体干细胞移植方案(MEL100,n=126)。主要终点为总体存活率。采用意向治疗分析。本试验已在ClinicalTrials.gov注册,注册号为NCT00367185。
结果中位随访51.5个月(IQR34.4~63.2),中位总体存活时间:MP组为33.2个月(13.8~54.8),MPT组为51.6个月(26.6~未到达),MELl00组为38.3个月(13.0~61.6)。在中位总体存活率方面,MPT方案优于MP方案(HR0.59,95%C10.46~0.81:P=0.0006)或MEL100方案(HR0.69,95%C10.49~0.96;P=0.027)。而MEL100方案和MP方案无差异(HR0.86,95%C10.65~1.15;P=0.32)。
结论试验结果提示,MPT方案目前应作为老年多发性骨髓瘤初治的推荐方案。 | Thierry Facon Jean Yves Mary Cyrille Hulin Lotfi Benboubker Michel Attal Brigitte Pegourie Marc Renaud Jean Luc Harou-sseau Gaelle Guillerm Carine Chaleteix Mamoun Dib Laurent Voillat Herve Maisonneuve Jacques Troncy Veronique Dorv-aux Mathieu Monconduit Claude Martin Philippe Casassus Jerome Jaubert Henry Jardel Chantal Doyen Brigitte Kolb Bruno Anglaret Bernard Grosbois Ibrahim Yakoub-Agha Claire Mathiot Herve Avet-Loiseau 王鸿鹄(译) | 2008 | 世界临床医学2008，2，1： | 0 |
| 6 | Identifying associations between epidemiological entities in news data for animal disease surveillance显示文摘Event-based surveillance systems are at the crossroads of human and animal(and plant and ecosystem)health,epidemiology,statistics,and informatics.Thus,their deployment faces many challenges specific to each domain and their intersections,such as relations among automation,artificial intelligence,and expertise.In this context,ourwork pertins to the extraction of epidemiological events in textual data(i.e.news)by unsupervised methods.We define the event extraction task as detecting pairs of epidemiological entities(e.g.a disease name and location).The quality of the ranked lists of pairs was evaluated using specific ranking evaluation metrics.We used a publicly available annotated corpus of 438 documents(i.e.news articles)related to animal disease events.The statistical approach was able to detect event-related pairs of epidemiological features with a good trade-off between precision and recall.Our results showed that using a window of words outperformed document-based and sentence-based approaches,while reducing the probability of detecting false pairs.Our results indicated that Mutual Information was less adapted than the Dice coefficient for ranking pairs of features in the event extraction framework.We believe that Mutual Information would be more relevant for rare pair detection(i.e.weak signals),but requires higher manual curation to avoid false positive extraction pairs.Moreover,generalising the country-level spatial features enabled better discrimination(i.e.ranking)of relevant disease-location pairs for event extraction. | Sarah Valentin Renaud Lancelot Mathieu Roche | 2021 | Artificial Intelligence in Agriculture2021，，1： | 0 |
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