| 1. |
- Kattge, Jens, et al.
(author)
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TRY plant trait database - enhanced coverage and open access
- 2020
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In: Global Change Biology. - : Wiley-Blackwell. - 1354-1013 .- 1365-2486. ; 26:1, s. 119-188
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Journal article (peer-reviewed)abstract
- Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
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| 2. |
- Gao, Hong, et al.
(author)
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The landscape of tolerated genetic variation in humans and primates
- 2023
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In: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 380:6648
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Journal article (peer-reviewed)abstract
- Personalized genome sequencing has revealed millions of genetic differences between individuals, but our understanding of their clinical relevance remains largely incomplete. To systematically decipher the effects of human genetic variants, we obtained whole-genome sequencing data for 809 individuals from 233 primate species and identified 4.3 million common protein-altering variants with orthologs in humans. We show that these variants can be inferred to have nondeleterious effects in humans based on their presence at high allele frequencies in other primate populations. We use this resource to classify 6% of all possible human protein-altering variants as likely benign and impute the pathogenicity of the remaining 94% of variants with deep learning, achieving state-of-the-art accuracy for diagnosing pathogenic variants in patients with genetic diseases.
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| 3. |
- Kuderna, Lukas F. K., et al.
(author)
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A global catalog of whole-genome diversity from 233 primate species
- 2023
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In: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 380:6648, s. 906-913
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Journal article (peer-reviewed)abstract
- The rich diversity of morphology and behavior displayed across primate species provides an informative context in which to study the impact of genomic diversity on fundamental biological processes. Analysis of that diversity provides insight into long-standing questions in evolutionary and conservation biology and is urgent given severe threats these species are facing. Here, we present high-coverage wholegenome data from 233 primate species representing 86% of genera and all 16 families. This dataset was used, together with fossil calibration, to create a nuclear DNA phylogeny and to reassess evolutionary divergence times among primate clades. We found within-species genetic diversity across families and geographic regions to be associated with climate and sociality, but not with extinction risk. Furthermore, mutation rates differ across species, potentially influenced by effective population sizes. Lastly, we identified extensive recurrence of missense mutations previously thought to be human specific. This study will open a wide range of research avenues for future primate genomic research.
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| 4. |
- Kuderna, Lukas F. K., et al.
(author)
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Identification of constrained sequence elements across 239 primate genomes
- 2024
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In: Nature. - : Springer Nature. - 0028-0836 .- 1476-4687. ; 625:7996, s. 735-742
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Journal article (peer-reviewed)abstract
- Noncoding DNA is central to our understanding of human gene regulation and complex diseases1,2, and measuring the evolutionary sequence constraint can establish the functional relevance of putative regulatory elements in the human genome3,4,5,6,7,8,9. Identifying the genomic elements that have become constrained specifically in primates has been hampered by the faster evolution of noncoding DNA compared to protein-coding DNA10, the relatively short timescales separating primate species11, and the previously limited availability of whole-genome sequences12. Here we construct a whole-genome alignment of 239 species, representing nearly half of all extant species in the primate order. Using this resource, we identified human regulatory elements that are under selective constraint across primates and other mammals at a 5% false discovery rate. We detected 111,318 DNase I hypersensitivity sites and 267,410 transcription factor binding sites that are constrained specifically in primates but not across other placental mammals and validate their cis-regulatory effects on gene expression. These regulatory elements are enriched for human genetic variants that affect gene expression and complex traits and diseases. Our results highlight the important role of recent evolution in regulatory sequence elements differentiating primates, including humans, from other placental mammals.
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| 5. |
- da Silva, Fabiano G., et al.
(author)
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Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images
- 2022
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In: Remote Sensing. - : MDPI. - 2072-4292. ; 14:13
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Journal article (peer-reviewed)abstract
- This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal–Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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| 6. |
- da Silva, Fabiano G., et al.
(author)
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Hybrid Feature Extraction Based on PCA and CNN for Oil Rig Classification in C-Band SAR Imagery
- 2022
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In: Proceedings of SPIE - The International Society for Optical Engineering. - : SPIE - International Society for Optical Engineering.
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Conference paper (peer-reviewed)abstract
- Feature extraction techniques play an essential role in classifying and recognizing targets in synthetic aperture radar (SAR) images. This article proposes a hybrid feature extraction technique based on convolutional neural networks and principal component analysis. The proposed method is used to extract features of oil rigs and ships in C-band synthetic aperture radar polarimetric images obtained with the Sentinel-1 satellite system. The extracted features are used as input in the logistic regression (LR), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision tree (DT), and k-nearest-neighbors (kNN) classification algorithms. Furthermore, the statistical tests of Kruskal-Wallis and Dunn were considered to show that the proposed extraction algorithm has a significant impact on the performance of the classifiers. © 2022 SPIE.
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| 7. |
- Moreira, André R., et al.
(author)
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Classification of Oil Rigs in SAR Images Using RPCA-Based Preprocessing
- 2024
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In: Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR. - : Institute of Electrical and Electronics Engineers (IEEE). - 9783800762873 ; , s. 432-437
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Conference paper (peer-reviewed)abstract
- This paper uses a signal separation method called Robust Principal Component Analysis (RPCA) as a pre-processing technique to improve the classification of oil rigs in Synthetic Aperture Radar (SAR) images. After the pre-processing method, features are extracted from the images using the VGG-16 convolutional neural network. These features guide classification through Support Vector Machine (SVM), Neural Networks, and Logistic Regression algorithms. The experiments used SAR images from the Sentinel-1 system, C-band, and VH polarization. Early results highlight that preprocessing improves classification accuracy compared to conventional methods. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.
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