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Stereotactic radiosurgery for haemorrhagic cerebral cavernous malformation:a multi-institutional,retrospective study

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Stroke & Vascular Neurology 2024年第3期9卷 221-229,I0130,I0131页

作者： Chloe Dumot Georgios Mantziaris Sam Dayawansa Zhiyuan Xu Stylianos Pikis Selcuk Peker Yavuz Samanci Gokce D Ardor Ahmed M Nabeel Wael A Reda Sameh R Tawadros Khaled Abdelkarim Amr M N El-Shehaby Reem M Emad Eldin Ahmed H Elazzazi Nuria Martínez Moreno Roberto MartínezÁlvarez Roman Liscak Jaromir May David Mathieu jean-nicolas tourigny Manjul Tripathi Akshay Rajput Narendra Kumar Rupinder Kaur Piero Picozzi Andrea Franzini Herwin Speckter Wenceslao Hernandez Anderson Brito Ronald E Warnick Juan Alzate Douglas Kondziolka Greg N Bowden Samir Patel Jason Sheehan Department of Neurological Surgery Hospices Civils de LyonLyonFrance Department of Neurological Surgery University of VirginiaCharlottesvilleVirginiaUSA Department of Neurosurgery Koc University School of MedicineIstanbulTurkey Gamma-knife Center Nasser institute HospitalCairoEgypt Neurosurgery Department Benha UniversityBenhaEgypt Departments of Neurosurgery Ain Shams UniversityCairoEgypt Department of Clinical Oncology Ain Shams UniversityCairoEgypt Department of Radiation Oncology National Cancer InstituteCairo UniversityGizaEgypt Faculty of Medicine Extended Modular ProgramAin Shams UniversityCairoEgypt Department of Radiosurgery Rúber Internacional HospitalMadridSpain Department of Stereotactic and Radiation Neurosurgery Na Homolce HospitalPragueCzech Republic Neurosurgery Centre de recherche du CHUSUniversitéde SherbrookeSherbrookeQuebecCanada Neurosurgery PGIMERChandigarhChandigarhIndia Radiation Therapy PGIMERChandigarhChandigarhIndia Neurosurgery IRCCS Istituto Clinico HumanitasRozzanoMilanItaly Dominican Gamma Knife Center and Radiology Department CEDIMATSanto DomingoDominican Republic Gamma Knife Center Mayfield ClinicThe Jewish Hospital-Mercy HealthCincinnatiOhioUSA Neurosurgery NYU Langone HealthNew YorkNew YorkUSA Neurosurgery University of AlbertaEdmontonAlbertaCanada Division of Radiation Oncology Department of OncologyUniversity of AlbertaEdmontonAlbertaCanada

Background Cerebral cavernous malformations(CCMs)frequently manifest with *** radiosurgery(SRS)has been employed for CCM not suitable for *** effect on reducing haemorrhage risk is still *** aim of this study was to expand on the safety and efficacy of SRS for haemorrhagic *** This retrospective multicentric study included CCM with at least one haemorrhage treated with single-session *** annual haemorrhagic rate(AHR)was calculated before and after *** event analysis and Cox regression were used to evaluate factors associated with *** radiation effects(AREs)and occurrence of new neurological deficits were *** The study included 381 patients (median age:37.5 years(Q1–Q3:25.8–51.9))with 414 *** AHR from diagnosis to SRS excluding the first haemorrhage was 11.08 per 100 CCM-years and was reduced to 2.7 per 100 CCM-years after *** recurrent event analysis,SRS,HR 0.27(95%CI 0.17 to 0.44),p13 Gy,HR 2.27(95%CI 1.20 to 4.32),p=0.012 and the presence of DVA,HR 2.08(95%CI 1.00 to 4.31),p=0.049 were factors associated with higher probability of post-SRS ***-SRS haemorrhage was symptomatic in 22 out of 381(5.8%)patients,presenting with transient(15/381)or permanent(7/381)neurological *** occurred in 11.1%(46/414)CCM and was responsible for transient neurological deficit in 3.9%(15/381)of the patients and permanent deficit in 1.1%(4/381)of the *** doses>13 Gy and CCM volume>0.7 cc were associated with increased risk of ARE.

关键词： doses haemorrhagic diagnosis

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Insertion of Ontological Knowledge to Improve Automatic Summarization Extraction Methods

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Journal of Intelligent Learning Systems and Applications 2011年第3期3卷 131-138页

作者： Jésus Antonio Motta Laurence Capus Nicole tourigny 不详

The vast availability of information sources has created a need for research on automatic summarization. Current methods perform either by extraction or abstraction. The extraction methods are interesting, because they are robust and independent of the language used. An extractive summary is obtained by selecting sentences of the original source based on information content. This selection can be automated using a classification function induced by a machine learning algorithm. This function classifies sentences into two groups: important or non-important. The important sentences then form the summary. But, the efficiency of this function directly depends on the used training set to induce it. This paper proposes an original way of optimizing this training set by inserting lexemes obtained from ontological knowledge bases. The training set optimized is reinforced by ontological knowledge. An experiment with four machine learning algorithms was made to validate this proposition. The improvement achieved is clearly significant for each of these algorithms.

关键词： Automatic Summarization Ontology Machine Learning Extraction Method

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Arctic Sea Ice: Decadal Simulations and Future Scenarios Using BESM-OA

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Atmospheric and Climate Sciences 2016年第2期6卷 351-366页

作者： Fernanda Casagrande Paulo Nobre Ronald Buss de Souza Andre Lanfer Marquez Etienne tourigny Vinicius Capistrano Raquel Leite Mello National Institute for Space Research Earth System Science Centre S&#227o José dos Campos Brazil National Institute for Space Research Centre for Weather Forecasting and Climate Research Cachoeira Paulista Brazil National Institute for Space Research Southern Center for Space Research Santa Maria Brazil

Important international reports and a significant number of scientific publications have reported on the abrupt decline of Arctic sea ice and its impact on the Global Climate System. In this paper, we evaluated the ability of the newly implemented Brazilian Earth System Model (BESM-OA) to represent Arctic sea ice and sensitivity to CO2 forcing, using decadal simulations (1980-2012) and future scenarios (2006-2100). We validated our results with satellite observations and compared them to Coupled Model Intercomparison Project, Phase 5 (CMIP5) for the same numerical experiment. BESM results for the seasonal cycle are consistent with CMIP5 models and observations. However, almost all models tend to overestimate sea ice extent in March compared to observations. The correct evaluation of minimum record of sea ice, in terms of time, spatial and area remains a limitation in Coupled Global Climate Models. Looking to spatial patterns, we found a systematic model error in September sea ice cover between the Beaufort Sea and East Siberia for most models. Future scenarios show a decrease in sea ice extent in response to an increase in radiative forcing for all models. From the year 2045 onwards, all models show a dramatic shrinking in sea ice and ice free conditions at the end of the melting season. The projected future sea ice loss is explained by the combined effects of the amplified warming in northern hemisphere high latitudes and feedbacks processes.

关键词： Arctic Sea Ice Climate Models Brazilian Earth System Model

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