Background:Giardia intestinalis is one of the most common causes of diarrhoea worldwide. Molecular techniques have greatly improved our understanding of the taxonomy and epidemiology of this parasite. Co-infection wit...Background:Giardia intestinalis is one of the most common causes of diarrhoea worldwide. Molecular techniques have greatly improved our understanding of the taxonomy and epidemiology of this parasite. Co-infection with mixed (sub-) assemblages has been reported, however, Sanger sequencing is sometimes unable to identify shared subtypes between samples involved in the same epidemiologically linked event, due to samples showing multiple dominant subtypes within the same outbreak. Here, we aimed to use a metabarcoding approach to uncover the genetic diversity within samples from sporadic and outbreak cases of giardiasis to characterise the subtype diversity, and determine if there are common sequences shared by epidemiologically linked cases that are missed by Sanger sequencing.Methods:We built a database with 1109 unique glutamate dehydrogenase (gdh) locus sequences covering most of the assemblages of G. intestinalis and used gdh metabarcoding to analyse 16 samples from sporadic and outbreak cases of giardiasis that occurred in New Zealand between 2010 and 2018.Results:There is considerable diversity of subtypes of G. intestinalis present in each sample. The utilisation of metabarcoding enabled the identification of shared subtypes between samples from the same outbreak. Multiple variants were identified in 13 of 16 samples, with Assemblage B variants most common, and Assemblages E and A present in mixed infections.Conclusions:This study showed that G. intestinalis infections in humans are frequently mixed, with multiple subtypes present in each host. Shared sequences among epidemiologically linked cases not identified through Sanger sequencing were detected. Considering the variation in symptoms observed in cases of giardiasis, and the potential link between symptoms and (sub-) assemblages, the frequency of mixed infections could have implications for our understanding of host–pathogen interactions.展开更多
3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines.Like many computer vision challenges,the 3D reconstruction task suffers from a lack o...3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines.Like many computer vision challenges,the 3D reconstruction task suffers from a lack of readily available training data in most domains,with methods typically depending on large datasets of high-quality image-model pairs.In this paper,we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain,and training is supplemented with different unlabelled datasets from the target real domain.We approach the problem of 3D reconstruction using volumetric regression and produce a training set of 25,000 pairs of images and volumes using hand-crafted 3D models of bananas rendered in a 3D modelling environment(Blender).Each image is then enhanced by a GAN to more closely match the domain of photographs of real images by introducing a volumetric consistency loss,improving performance of 3D reconstruction on real images.Our solution harnesses the cost benefits of synthetic data while still maintaining good performance on real world images.We focus this work on the task of 3D banana reconstruction from a single image,representing a common task in plant phenotyping,but this approach is general and may be adapted to any 3D reconstruction task including other plant species and organs.展开更多
基金This work was supported by funds from MicroAquaTech,Massey University,Royal Society Te Aparangi Grant RDF-MAU170New Zealand Ministry of Health Contract Number 355766-02The Percival Carmine Chair in Epidemiology and Public Health.
文摘Background:Giardia intestinalis is one of the most common causes of diarrhoea worldwide. Molecular techniques have greatly improved our understanding of the taxonomy and epidemiology of this parasite. Co-infection with mixed (sub-) assemblages has been reported, however, Sanger sequencing is sometimes unable to identify shared subtypes between samples involved in the same epidemiologically linked event, due to samples showing multiple dominant subtypes within the same outbreak. Here, we aimed to use a metabarcoding approach to uncover the genetic diversity within samples from sporadic and outbreak cases of giardiasis to characterise the subtype diversity, and determine if there are common sequences shared by epidemiologically linked cases that are missed by Sanger sequencing.Methods:We built a database with 1109 unique glutamate dehydrogenase (gdh) locus sequences covering most of the assemblages of G. intestinalis and used gdh metabarcoding to analyse 16 samples from sporadic and outbreak cases of giardiasis that occurred in New Zealand between 2010 and 2018.Results:There is considerable diversity of subtypes of G. intestinalis present in each sample. The utilisation of metabarcoding enabled the identification of shared subtypes between samples from the same outbreak. Multiple variants were identified in 13 of 16 samples, with Assemblage B variants most common, and Assemblages E and A present in mixed infections.Conclusions:This study showed that G. intestinalis infections in humans are frequently mixed, with multiple subtypes present in each host. Shared sequences among epidemiologically linked cases not identified through Sanger sequencing were detected. Considering the variation in symptoms observed in cases of giardiasis, and the potential link between symptoms and (sub-) assemblages, the frequency of mixed infections could have implications for our understanding of host–pathogen interactions.
基金the Engineering and Physical Sciences Research Council[EP/R513283/1]awarded to Zane K.J.Hartley。
文摘3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines.Like many computer vision challenges,the 3D reconstruction task suffers from a lack of readily available training data in most domains,with methods typically depending on large datasets of high-quality image-model pairs.In this paper,we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain,and training is supplemented with different unlabelled datasets from the target real domain.We approach the problem of 3D reconstruction using volumetric regression and produce a training set of 25,000 pairs of images and volumes using hand-crafted 3D models of bananas rendered in a 3D modelling environment(Blender).Each image is then enhanced by a GAN to more closely match the domain of photographs of real images by introducing a volumetric consistency loss,improving performance of 3D reconstruction on real images.Our solution harnesses the cost benefits of synthetic data while still maintaining good performance on real world images.We focus this work on the task of 3D banana reconstruction from a single image,representing a common task in plant phenotyping,but this approach is general and may be adapted to any 3D reconstruction task including other plant species and organs.
