| 1. |
- Cao, Jun, et al.
(författare)
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Achieving malaria elimination in China
- 2021
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Ingår i: The Lancet Public Health. - : Elsevier. - 2468-2667. ; 6:12, s. e871-e872
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
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| 2. |
- Elder, Leanne, et al.
(författare)
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Sixty-one thousand recent planktonic foraminifera from the Atlantic Ocean
- 2018
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Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 5, s. 180109-
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Tidskriftsartikel (refereegranskat)abstract
- Marine microfossils record the environmental, ecological, and evolutionary dynamics of past oceans in temporally expanded sedimentary archives. Rapid imaging approaches provide a means of exploiting the primary advantage of this archive, the vast number of fossils, for evolution and ecology. Here we provide the first large scale image and 2D and 3D shape dataset of modern planktonic foraminifera, a major microfossil group, from 34 Atlantic Ocean sediment samples. Information on more than 124,000 objects is provided, including general object classification for 4/5ths of the dataset (~ 99,000 objects). Of the ~ 99,000 classifications provided, more than 61,000 are complete or damaged planktonic foraminifera. Objects also include benthic foraminifera, ostracods, pteropods, spicules, and planktonic foraminifera test fragments, among others. This dataset is the first major microfossil output of a new high-throughput imaging method (AutoMorph) developed to extract 2D and 3D data from photographic images of fossils. Our sample preparation and imaging techniques are described in detail. The data provided here comprises the most extensive publically available archive of planktonic foraminiferal morphology and morphological variation to date.
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| 3. |
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| 4. |
- Hsiang, Allison
(författare)
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AutoMorph: Accelerating morphometrics with automated 2D and 3D image processing and shape extraction
- 2017
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Ingår i: Methods in Ecology and Evolution. - 2041-210X.
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Tidskriftsartikel (refereegranskat)abstract
- Large-scale, comparative studies of morphological variation are rare due to the time-intensive nature of shape quantification. This data gap is important to address, as intraspecific and interspecific morphological variation underpins and reflects ecological and evolutionary processes.Here, we detail a novel software package, AutoMorph, for high-throughput object and shape extraction. AutoMorph can batch image many types of organisms (e.g. foraminifera, molluscs and fish teeth), allowing for rapid generation of assemblage- scale morphological data.We used AutoMorph to image and generate 2D and 3D morphological data for >100,000 marine microfossils in about a year. Our collaborators have used AutoMorph to process >12,000 patellogastropod shells and >50,000 fish teeth.AutoMorph allows users to rapidly produce large amounts of morphological data, facilitating community-scale evolutionary and ecological studies. To hasten the adoption of automated approaches, we have made AutoMorph freely available and open source. AutoMorph runs on all UNIX-like systems; future versions will run across all platforms.
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| 5. |
- Kahanamoku, Sara, et al.
(författare)
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Twelve thousand recent patellogastropods from a northeastern Pacific latitudinal gradient
- 2018
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Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 5, s. 170197-
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Tidskriftsartikel (refereegranskat)abstract
- Marine microfossils record the environmental, ecological, and evolutionary dynamics of past oceans in temporally expanded sedimentary archives. Rapid imaging approaches provide a means of exploiting the primary advantage of this archive, the vast number of fossils, for evolution and ecology. Here we provide the first large scale image and 2D and 3D shape dataset of modern planktonic foraminifera, a major microfossil group, from 34 Atlantic Ocean sediment samples. Information on more than 124,000 objects is provided, including general object classification for 4/5ths of the dataset (~ 99,000 objects). Of the ~ 99,000 classifications provided, more than 61,000 are complete or damaged planktonic foraminifera. Objects also include benthic foraminifera, ostracods, pteropods, spicules, and planktonic foraminifera test fragments, among others. This dataset is the first major microfossil output of a new high-throughput imaging method (AutoMorph) developed to extract 2D and 3D data from photographic images of fossils. Our sample preparation and imaging techniques are described in detail. The data provided here comprises the most extensive publically available archive of planktonic foraminiferal morphology and morphological variation to date.
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| 6. |
- Karaderi, Tayfun, et al.
(författare)
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Visual Microfossil Identification via Deep Metric Learning
- 2022
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Ingår i: Pattern Recognition and Artificial Intelligence. - Cham : Springer. - 9783031090363 - 9783031090370 ; , s. 34-46
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Konferensbidrag (refereegranskat)abstract
- We apply deep metric learning for the first time to the problem of classifying planktic foraminifer shells on microscopic images. This species recognition task is an important information source and scientific pillar for reconstructing past climates. All foraminifer CNN recognition pipelines in the literature produce black-box classifiers that lack visualisation options for human experts and cannot be applied to open set problems. Here, we benchmark metric learning against these pipelines, produce the first scientific visualisation of the phenotypic planktic foraminifer morphology space, and demonstrate that metric learning can be used to cluster species unseen during training. We show that metric learning outperforms all published CNN-based state-of-the-art benchmarks in this domain. We evaluate our approach on the 34,640 expert-annotated images of the Endless Forams public library of 35 modern planktic foramini-fera species. Our results on this data show leading 92%92% accuracy (at 0.84 F1-score) in reproducing expert labels on withheld test data, and 66.5%66.5% accuracy (at 0.70 F1-score) when clustering species never encountered in training. We conclude that metric learning is highly effective for this domain and serves as an important tool towards expert-in-the-loop automation of microfossil identification. Key code, network weights, and data splits are published with this paper for full reproducibility.
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| 7. |
- Lu, Shen-ning, et al.
(författare)
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Application of an innovative grid-based surveillance strategy to ensure elimination and prevent reintroduction of malaria in high-risk border communities in China
- 2022
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Ingår i: BMC Public Health. - : Springer Nature. - 1471-2458. ; 22:1
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Tidskriftsartikel (refereegranskat)abstract
- Grid management is a grassroots governance strategy widely implemented in China since 2004 to improve the government's efficiency to actively find and solve problems among populated regions. A grid-based strategy surveillancing high-risk groups, including mobile and migrant populations (MMPs), in the China-Myanmar border region has played an indispensable role in promoting and consolidating the malaria elimination efforts by tracking and timely identification of potential importation or re-establishment of malaria among MMPs. A sequential mixed methods was implementated to explore the operational mechanism and best practices of the grid-based strategy including through the focus group discussions (FGDs), comparison of before and after the implementation of a grid-based strategy in the field sites, and data collection from the local health system.This paper distills the implementation mechanism and highlights the role of the grid-based strategy in the elimination and prevention of re-establishment of malaria transmission.
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| 8. |
- Nielson, Carrie M., et al.
(författare)
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Novel Genetic Variants Associated With Increased Vertebral Volumetric BMD, Reduced Vertebral Fracture Risk, and Increased Expression of SLC1A3 and EPHB2
- 2016
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Ingår i: Journal of Bone and Mineral Research. - : Wiley. - 0884-0431. ; 31:12, s. 2085-2097
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWASs) have revealed numerous loci for areal bone mineral density (aBMD). We completed the first GWAS meta-analysis (n=15,275) of lumbar spine volumetric BMD (vBMD) measured by quantitative computed tomography (QCT), allowing for examination of the trabecular bone compartment. SNPs that were significantly associated with vBMD were also examined in two GWAS meta-analyses to determine associations with morphometric vertebral fracture (n=21,701) and clinical vertebral fracture (n=5893). Expression quantitative trait locus (eQTL) analyses of iliac crest biopsies were performed in 84 postmenopausal women, and murine osteoblast expression of genes implicated by eQTL or by proximity to vBMD-associated SNPs was examined. We identified significant vBMD associations with five loci, including: 1p36.12, containing WNT4 and ZBTB40; 8q24, containing TNFRSF11B; and 13q14, containing AKAP11 and TNFSF11. Two loci (5p13 and 1p36.12) also contained associations with radiographic and clinical vertebral fracture, respectively. In 5p13, rs2468531 (minor allele frequency [MAF]=3%) was associated with higher vBMD (β=0.22, p=1.9×10-8) and decreased risk of radiographic vertebral fracture (odds ratio [OR]=0.75; false discovery rate [FDR] p=0.01). In 1p36.12, rs12742784 (MAF=21%) was associated with higher vBMD (β=0.09, p=1.2×10-10) and decreased risk of clinical vertebral fracture (OR=0.82; FDR p=7.4×10-4). Both SNPs are noncoding and were associated with increased mRNA expression levels in human bone biopsies: rs2468531 with SLC1A3 (β=0.28, FDR p=0.01, involved in glutamate signaling and osteogenic response to mechanical loading) and rs12742784 with EPHB2 (β=0.12, FDR p=1.7×10-3, functions in bone-related ephrin signaling). Both genes are expressed in murine osteoblasts. This is the first study to link SLC1A3 and EPHB2 to clinically relevant vertebral osteoporosis phenotypes. These results may help elucidate vertebral bone biology and novel approaches to reducing vertebral fracture incidence.
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| 9. |
- Plavetic, Marko, et al.
(författare)
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Identification of environmentally relevant benthic foraminifera from the Skagerrak fjords by deep learning image modelling
- 2023
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Ingår i: International Congress FORAMS2023, Perugia, Italy 25-30th June, 2023.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Over the several past decades there has been increasing interest in using foraminifera as environmental indicators for coastal marine environments. As compared to macrofauna, which are currently used in environmental studies, foraminifera offer several distinct advantages as bioindicators, including short generation times, high number of individuals per small sample volume, hard and durable tests with high preservation potential, and low cost of sample extraction. One of the main problems with foraminifera identification is reliance on manual identification and expert judgement, which is a tedious and slow process prone to errors and subjectivity. Deep learning, a subfield of machine learning, has emerged as a promising solution to this challenge, since a neural network can learn to recognize subtle differences in shell morphology that may be difficult for the human eye to distinguish. Benthic foraminifera mounted on microslides from several Skagerrak fjords including Gullmar Fjord, Hakefjord and Idefjord were imaged using a Nikon SMZ-10 stereomicroscope and DeltaPix DP450 microscope camera. Images were then processed in Roboflow API, where individual foraminifera were labelled and classified. This resulted in 3003 images and 22 138 labelled individuals. Using the labeled images, a dataset was created to be used for deep learning training. We used the YOLO (You Only Look Once) v7 model implemented in the PyTorch framework, which has demonstrated state-of-the-art speed and performance for object detection as of the time of writing. Models were trained using a Nvidia RTX A4000 GPU (graphical processing unit). Preliminary results show a 90,3% mAP (mean average precision) and 78,8% mAP on the best and the worst performing models, respectively. Even though the imaging and labelling was done in a short amount of time, the results look promising and show that even a relatively small dataset can be used for training a reliable deep learning species identification model.
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