Structured Pointcloud Segmentation for Individual Mangrove Tree Modeling
Ontology highlight
ABSTRACT:
SUBMITTER: Figueroa Mora K
PROVIDER: S-EPMC7297589 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Mangroves are ecosystems located in the transition zone between land and sea that serve as a potential source of biotechnological resources. Brazil's extensive coast contains one of the largest mangrove forests in the world (encompassing an area of 25,000km2 along all the coast). Endophytic bacteria were isolated from the following three plant species: Rhizophora mangle, Laguncularia racemosa and Avicennia nitida. A large number of these isolates, 115 in total, were evaluated for their ability to fix nitrogen and solubilize phosphorous. Bacteria that tested positive for both of these tests were examined further to determine their level of indole acetic acid production. Two strains with high indole acetic acid production were selected for use as inoculants for reforestation trees, and then the growth of the plants was evaluated under field conditions. The bacterium Pseudomonas fluorescens (strain MCR1.10) had a low phosphorus solubilization index, while this index was higher in the other strain used, Enterobacter sp. (strain MCR1.48). We used the reforestation tree Acacia polyphylla. The results indicate that inoculation with the MCR1.48 endophyte increases Acacia polyphylla shoot dry mass, demonstrating that this strain effectively promotes the plant's growth and fitness, which can be used in the seedling production of this tree. Therefore, we successfully screened the biotechnological potential of endophyte isolates from mangrove, with a focus on plant growth promotion, and selected a strain able to provide limited nutrients and hormones for in plant growth.
| S-EPMC5790640 | biostudies-literature
Project description:Environmental variation along the geographical space can shape populations by natural selection. In the context of global warming and changing precipitation regimes, it is crucial to understand the role of environmental heterogeneity in tropical trees adaptation, given their disproportional contribution to water and carbon biogeochemical cycles. Here, we investigated how heterogeneity in freshwater availability along tropical wetlands has influenced molecular variations of the black mangrove (Avicennia germinans). A total of 57 trees were sampled at seven sites differing markedly in precipitation regime and riverine freshwater inputs. Using 2,297 genome‐wide single nucleotide polymorphic markers, we found signatures of natural selection by the association between variations in allele frequencies and environmental variables, including the precipitation of the warmest quarter and the annual precipitation. Additionally, we found candidate loci for selection based on statistical deviations from neutral expectations of interpopulation differentiation. Most candidate loci within transcribed sequences were functionally associated with central aspects of drought tolerance or plant response to drought. Moreover, our results suggest the occurrence of the rapid evolution of a population, probably in response to sudden and persistent limitations in plant access to soil water, following a road construction in 1974. Observations supporting rapid evolution included the reduction in tree size and changes in allele frequencies and in transcript expression associated with increased drought tolerance through the accumulation of osmoprotectants and antioxidants, biosynthesis of cuticles, protection against protein degradation, stomatal closure, photorespiration and photosynthesis. We describe a major role of spatial heterogeneity in freshwater availability in the specialization of this typically tropical tree.
2020-05-15 | GSE123659 | GEO
Project description:A tree-structured multiplicative gamma process (TMGP) is developed, for inferring the depth of a tree-based factor-analysis model. This new model is coupled with the nested Chinese restaurant process, to nonparametrically infer the depth and width (structure) of the tree. In addition to developing the model, theoretical properties of the TMGP are addressed, and a novel MCMC sampler is developed. The structure of the inferred tree is used to learn relationships between high-dimensional data, and the model is also applied to compressive sensing and interpolation of incomplete images.
| S-EPMC4180059 | biostudies-literature
Project description:MOTIVATION:While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. RESULTS:We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. AVAILABILITY AND IMPLEMENTATION:Dataset is freely available at: https://goo.gl/cNM4EL. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
| S-EPMC6748796 | biostudies-literature
Project description:We develop a general statistical framework for the analysis and inference of large tree-structured data, with a focus on developing asymptotic goodness-of-fit tests. We first propose a consistent statistical model for binary trees, from which we develop a class of invariant tests. Using the model for binary trees, we then construct tests for general trees by using the distributional properties of the Continuum Random Tree, which arises as the invariant limit for a broad class of models for tree-structured data based on conditioned Galton-Watson processes. The test statistics for the goodness-of-fit tests are simple to compute and are asymptotically distributed as χ 2 and F random variables. We illustrate our methods on an important application of detecting tumour heterogeneity in brain cancer. We use a novel approach with tree-based representations of magnetic resonance images and employ the developed tests to ascertain tumor heterogeneity between two groups of patients.
| S-EPMC10066867 | biostudies-literature
Project description:We describe and analyze a simple and effective algorithm for sequence segmentation applied to speech processing tasks. We propose a neural architecture that is composed of two modules trained jointly: a recurrent neural network (RNN) module and a structured prediction model. The RNN outputs are considered as feature functions to the structured model. The overall model is trained with a structured loss function which can be designed to the given segmentation task. We demonstrate the effectiveness of our method by applying it to two simple tasks commonly used in phonetic studies: word segmentation and voice onset time segmentation. Results suggest the proposed model is superior to previous methods, obtaining state-of-the-art results on the tested datasets.
| S-EPMC5638122 | biostudies-literature
Project description:Mangrove trees occur in a variety of geomorphic and sedimentary settings. Yet, studies investigating their role as habitat providers often focus on the most common biophysical types, such as deltaic, estuarine, open coast or lagoonal mangroves on soft sediments, disregarding less typical environments. Here, we investigated the influence of individual mangrove trees growing on a consolidated backreef system (Laucala Bay, Fiji) on habitat use by reef fishes. Combining field surveys and an experiment, we quantified the extent to which reef mangrove trees serve as habitat for solitary or shoaling reef fishes. Using mangrove tree mimics, we disentangled effects attributable to the physical structure of trees from those related to their bio-chemical properties. We found that fish numbers were 3.7 times higher within close proximity to the mangrove trees than at control sites and correlated significantly with root system perimeter. The roots of larger trees sheltered aggregations of juveniles and adults at incoming and high tides. Mangrove trees and mimics attracted fishes alike. We show that mangrove trees on backreefs provide habitat for shoaling and adult reef fishes in addition to serving as nursery areas, an ecosystem service otherwise lacking on backreef areas with low structural complexity.
| S-EPMC11316738 | biostudies-literature
Project description:This paper is about an algorithm, FlexTree, for general supervised learning. It extends the binary tree-structured approach (Classification and Regression Trees, CART) although it differs greatly in its selection and combination of predictors. It is particularly applicable to assessing interactions: gene by gene and gene by environment as they bear on complex disease. One model for predisposition to complex disease involves many genes. Of them, most are pure noise; each of the values that is not the prevalent genotype for the minority of genes that contribute to the signal carries a "score." Scores add. Individuals with scores above an unknown threshold are predisposed to the disease. For the additive score problem and simulated data, FlexTree has cross-validated risk better than many cutting-edge technologies to which it was compared when small fractions of candidate genes carry the signal. For the model where only a precise list of aberrant genotypes is predisposing, there is not a systematic pattern of absolute superiority; however, overall, FlexTree seems better than the other technologies. We tried the algorithm on data from 563 Chinese women, 206 hypotensive, 357 hypertensive, with information on ethnicity, menopausal status, insulin-resistant status, and 21 loci. FlexTree and Logic Regression appear better than the others in terms of Bayes risk. However, the differences are not significant in the usual statistical sense.
| S-EPMC489971 | biostudies-literature
Project description:An international data science challenge, called National Ecological Observatory Network-National Institute of Standards and Technology data science evaluation, was set up in autumn 2017 with the goal to improve the use of remote sensing data in ecological applications. The competition was divided into three tasks: (1) individual tree crown (ITC) delineation, for identifying the location and size of individual trees; (2) alignment between field surveyed trees and ITCs delineated on remote sensing data; and (3) tree species classification. In this paper, the methods and results of team Fondazione Edmund Mach (FEM) are presented. The ITC delineation (Task 1 of the challenge) was done using a region growing method applied to a near-infrared band of the hyperspectral images. The optimization of the parameters of the delineation algorithm was done in a supervised way on the basis of the Jaccard score using the training set provided by the organizers. The alignment (Task 2) between the delineated ITCs and the field surveyed trees was done using the Euclidean distance among the position, the height, and the crown radius of the ITCs and the field surveyed trees. The classification (Task 3) was performed using a support vector machine classifier applied to a selection of the hyperspectral bands and the canopy height model. The selection of the bands was done using the sequential forward floating selection method and the Jeffries Matusita distance. The results of the three tasks were very promising: team FEM ranked first in the data science competition in Task 1 and 2, and second in Task 3. The Jaccard score of the delineated crowns was 0.3402, and the results showed that the proposed approach delineated both small and large crowns. The alignment was correctly done for all the test samples. The classification results were good (overall accuracy of 88.1%, kappa accuracy of 75.7%, and mean class accuracy of 61.5%), although the accuracy was biased toward the most represented species.
| S-EPMC6330952 | biostudies-literature
Project description:In this paper, we present a multi-atlas-based framework for accurate, consistent and simultaneous segmentation of a group of target images. Multi-atlas-based segmentation algorithms consider concurrently complementary information from multiple atlases to produce optimal segmentation outcomes. However, the accuracy of these algorithms relies heavily on the precise alignment of the atlases with the target image. In particular, the commonly used pairwise registration may result in inaccurate alignment especially between images with large shape differences. Additionally, when segmenting a group of target images, most current methods consider these images independently with disregard of their correlation, thus resulting in inconsistent segmentations of the same structures across different target images. We propose two novel strategies to address these limitations: 1) a novel tree-based groupwise registration method for concurrent alignment of both the atlases and the target images, and 2) an iterative groupwise segmentation method for simultaneous consideration of segmentation information propagated from all available images, including the atlases and other newly segmented target images. Evaluation based on various datasets indicates that the proposed multi-atlas-based multi-image segmentation (MABMIS) framework yields substantial improvements in terms of consistency and accuracy over methods that do not consider the group of target images holistically.
| S-EPMC3195928 | biostudies-other