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High frequency of latent Chlamydia trachomatis infection in patients with rhegmatogenous retinal detachment

International Journal of Ophthalmology(English edition) 2016年第6期9卷 863-868页

作者： Ernest V.Bolko alexei l.pozniak Dmitrii S.Maltsev alexei A.Suetov Irina V.Nuralova St.Petersburg Branch of the Academician S.Fyodorov IRTC "Eye Microsurgery"St.Petersburg 192283Russia Department of Ophthalmology Military Medical AcademySt.Petersburg 194044Russia

AIM： To determine the frequency of detection of ocular and extraocular Chlamydia trachomatis （CT） infection in non -high myopes with rhegmatogenous retinal detachment （RRD）. METHODS： This was a single-center, nonrandomized, prospective, case-control study. One hundred and four patients were divided into a study group with RRD （n= 63） and a control group with traumatic retinal detachment （n=41）. Samples of subretinal fluid （SFR）, conjunctival, urethral/cervical swabs, and blood were collected. The frequency of detection of CT infection in SRF samples was determined by polymerase chain reaction （PCR）, direct fluorescence assay （DFA） and cell culture, whereas that in conjunctival swabs was determined by PCR and DFA, and those in urethral/cervical swabs and blood were determined by DFA. Yates Chi-square test （with Bonferroni correction） and two-tailed Student＇s t-test were used for statistical analysis. RESUlTS： SRF CT infection was detected more frequently in the study group （50.8%-71.4%） than in the control group （9.8%-12.2%） by all the methods used （P〈 0.01）. The frequency of detection of conjunctival CT infection by DFA was higher in the RRD patients compared with the controls （81.0% vs 24.4%, P=0.004）. The PCR detected conjunctival CT infection more often in the study group than in the controls （46.0% vs89.8%, P= 0.007）. The DFA detected CT in blood specimens almost as frequently as in urogenital specimens, for the RRD patients （61.2% vs 63.5%） and the controls （7.3% vs 9.8%）. CONClUSION： CT infection is detected with high frequency in non-high myopes with RRD.

关键词： rhegmatogenous retinal detachment Chlamydia trachomatis myopia latent infection

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Global Wheat Head Detection(GWHD)Dataset:A large and Diverse Dataset of High-Resolution RGB-labelled Images to Develop and Benchmark Wheat Head Detection Methods

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Plant Phenomics 2020年第1期2卷 243-254页

作者： Etienne David Simon Madec Pouria Sadeghi-Tehran Helge Aasen Bangyou Zheng Shouyang liu Norbert Kirchgessner Goro Ishikawa Koichi Nagasawa Minhajul A.Badhon Curtis pozniak Benoit de Solan Andreas Hund Scott C.Chapman Frédéric Baret Ian Stavness Wei Guo Arvalis Institut du végétal3 Rue Joseph et Marie Hackin75116 ParisFrance UMR1114 EMMAH INRAECentre PACABâtiment ClimatDomaine Saint-Paul228 Route de l’AérodromeCS 4050984914 Avignon CedexFrance Plant Sciences Department Rothamsted ResearchHarpendenUK Institute of Agricultural Sciences ETH ZurichUniversitätstrasse 28092 ZurichSwitzerland CSIRO Agriculture and Food Queensland Biosciences Precinct306 Carmody RoadSt Lucia4067 QLDAustralia Plant Phenomics Research Center Nanjing Agricultural UniversityNanjingChina Institute of Crop Science National Agriculture and Food Research OrganizationJapan Hokkaido Agricultural Research Center National Agriculture and Food Research OrganizationJapan Department of Computer Science University of SaskatchewanCanada Department of Plant Sciences University of SaskatchewanCanada School of Food and Agricultural Sciences The University of QueenslandGatton4343 QLDAustralia Graduate School of Agricultural and life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan

The detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health,size,maturity stage,and the presence of *** studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning ***,these methods have generally been calibrated and validated on limited *** variability in observational conditions,genotypic differences,development stages,and head orientation makes wheat head detection a challenge for computer ***,possible blurring due to motion or wind and overlap between heads for dense populations make this task even more *** a joint international collaborative effort,we have built a large,diverse,and well-labelled dataset of wheat images,called the Global Wheat Head Detection(GWHD)*** contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of *** for image acquisition,associating minimum metadata to respect FAIR principles,and consistent head labelling methods are proposed when developing new head detection *** GWHD dataset is publicly available at http://***/and aimed at developing and benchmarking methods for wheat head detection.

关键词： Wheat wheat maturity

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Global Wheat Head Detection 2021:An Improved Dataset for Benchmarking Wheat Head Detection Methods

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Plant Phenomics 2021年第1期3卷 277-285页

作者： Etienne David Mario Serouart Daniel Smith Simon Madec Kaaviya Velumani Shouyang liu Xu Wang Francisco Pinto Shahameh Shafiee Izzat SATahir Hisashi Tsujimoto Shuhei Nasuda Bangyou Zheng Norbert Kirchgessner Helge Aasen Andreas Hund Pouria Sadhegi-Tehran Koichi Nagasawa Goro Ishikawa Sébastien Dandrifosse Alexis Carlier Benjamin Dumont Benoit Mercatoris Byron Evers Ken Kuroki Haozhou Wang Masanori Ishii Minhajul ABadhon Curtis pozniak David Shaner leBauer Morten lillemo Jesse Poland Scott Chapman Benoit de Solan Frédéric Baret Ian Stavness Wei Guo Arvalis Institut du Végétal3 Rue Joseph et Marie Hackin75116 ParisFrance UMR1114 EMMAH INRAECentre PACABâtiment ClimatDomaine Saint-Paul228 Route de l’AérodromeCS 4050984914 Avignon CedexFrance School of Food and Agricultural Sciences The University of QueenslandGatton4343 QLDAustralia Hiphen SAS 120 Rue Jean DaussetAgroparcBâtiment Technicité84140 AvignonFrance Plant Phenomics Research Center Nanjing Agricultural UniversityNanjingChina Wheat Genetics Resource Center Dep.of Plant PathologyKansas State Univ.4024 Throckmorton Plant Sciences CenterManhattanKansasUSA Global Wheat Program International Maize and Wheat Improvement Centre(CIMMYT)MexicoD.F.Mexico Faculty of Biosciences Norwegian University of Life SciencesP.O.Box 5003NO-1432ÅsNorway Agricultural Research Corporation Wheat Research ProgramP.O.Box 126Wad MedaniSudan Arid land Research Center Tottori UniversityTottori 680-0001Japan laboratories of Plant Genetics and Plant Breeding Graduate School of AgricultureKyoto UniversityJapan CSIRO Agriculture and Food Queensland Biosciences Precinct306 Carmody RoadSt Lucia4067 QLDAustralia Institute of Agricultural Sciences ETH ZurichUniversitätstrasse 28092 ZurichSwitzerland Plant Sciences Department Rothamsted ResearchHarpendenUK Institute of Crop Science National Agriculture and Food Research OrganizationJapan Hokkaido Agricultural Research Center National Agriculture and Food Research OrganizationJapan Biosystems Dynamics and Exchanges TERRA Teaching and Research CenterGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium Plant Sciences TERRA Teaching and Research CenterGembloux Agro-Bio TechUniversity of Liège5030 GemblouxBelgium Graduate School of Agricultural and life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan Department of Computer Science University of SaskatchewanCanada Department of Plant Sciences University of SaskatchewanCanada College of Agriculture and life Sciences University of ArizonaTucsonArizona

The Global Wheat Head Detection(GWHD)dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/*** an associated competition hosted in Kaggle,GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science *** this first experience,a few avenues for improvements have been identified regarding data size,head diversity,and label *** address these issues,the 2020 dataset has been reexamined,relabeled,and complemented by adding 1722 images from 5 additional countries,allowing for 81,553 additional wheat *** now release in 2021 a new version of the Global Wheat Head Detection dataset,which is bigger,more diverse,and less noisy than the GWHD_2020 version.

关键词： Wheat adding release

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