Dataset Information

Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods.

ABSTRACT: For developing global strategies against the dramatic spread of invasive species, we need to identify the geographical, environmental, and socioeconomic factors determining the spatial distribution of invasive species. In our study, we investigated these factors influencing the occurrences of common milkweed (Asclepias syriaca L.), an invasive plant species that is of great concern to the European Union (EU). In a Hungarian study area, we used country-scale soil and climate databases, as well as an EU-scale land cover databases (CORINE) for the analyses. For the abundance data of A. syriaca, we applied the field survey photos from the Land Use and Coverage Area Frame Survey (LUCAS) Land Cover database for the European Union. With machine learning algorithm methods, we quantified the relative weight of the environmental variables on the abundance of common milkweed. According to our findings, soil texture and soil type (sandy soils) were the most important variables determining the occurrence of this species. We could exactly identify the actual land cover types and the recent land cover changes that have a significant role in the occurrence the common milkweed in Europe. We could also show the role of climatic conditions of the study area in the occurrence of this species, and we could prepare the potential distribution map of common milkweed for the study area.

SUBMITTER: Szilassi P

PROVIDER: S-EPMC6963816 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Understanding the Environmental Background of an Invasive Plant Species (<i>Asclepias syriaca</i>) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods.

Szilassi Péter P Szatmári Gábor G Pásztor László L Árvai Mátyás M Szatmári József J Szitár Katalin K Papp Levente L

Plants (Basel, Switzerland) 20191212 12

For developing global strategies against the dramatic spread of invasive species, we need to identify the geographical, environmental, and socioeconomic factors determining the spatial distribution of invasive species. In our study, we investigated these factors influencing the occurrences of common milkweed (<i>Asclepias syriaca</i> L.), an invasive plant species that is of great concern to the European Union (EU). In a Hungarian study area, we used country-scale soil and climate databases, as ...[more]

PMID: 31842272

Similar Datasets

Project description:BACKGROUND:Milkweeds (Asclepias L.) have been extensively investigated in diverse areas of evolutionary biology and ecology; however, there are few genetic resources available to facilitate and compliment these studies. This study explored how low coverage genome sequencing of the common milkweed (Asclepias syriaca L.) could be useful in characterizing the genome of a plant without prior genomic information and for development of genomic resources as a step toward further developing A. syriaca as a model in ecology and evolution. RESULTS:A 0.5× genome of A. syriaca was produced using Illumina sequencing. A virtually complete chloroplast genome of 158,598 bp was assembled, revealing few repeats and loss of three genes: accD, clpP, and ycf1. A nearly complete rDNA cistron (18S-5.8S-26S; 7,541 bp) and 5S rDNA (120 bp) sequence were obtained. Assessment of polymorphism revealed that the rDNA cistron and 5S rDNA had 0.3% and 26.7% polymorphic sites, respectively. A partial mitochondrial genome sequence (130,764 bp), with identical gene content to tobacco, was also assembled. An initial characterization of repeat content indicated that Ty1/copia-like retroelements are the most common repeat type in the milkweed genome. At least one A. syriaca microread hit 88% of Catharanthus roseus (Apocynaceae) unigenes (median coverage of 0.29×) and 66% of single copy orthologs (COSII) in asterids (median coverage of 0.14×). From this partial characterization of the A. syriaca genome, markers for population genetics (microsatellites) and phylogenetics (low-copy nuclear genes) studies were developed. CONCLUSIONS:The results highlight the promise of next generation sequencing for development of genomic resources for any organism. Low coverage genome sequencing allows characterization of the high copy fraction of the genome and exploration of the low copy fraction of the genome, which facilitate the development of molecular tools for further study of a target species and its relatives. This study represents a first step in the development of a community resource for further study of plant-insect co-evolution, anti-herbivore defense, floral developmental genetics, reproductive biology, chemical evolution, population genetics, and comparative genomics using milkweeds, and A. syriaca in particular, as ecological and evolutionary models.

Project description:BackgroundAnimals fertilize thousands of angiosperm species whose floral-display sizes can significantly influence pollinator behavior and plant reproductive success. Many studies have measured the interactions among pollinator behavior, floral-display size, and plant reproductive success, but few studies have been able to separate the effects of pollinator behavior and post-pollination processes on angiosperm sexual reproduction. In this study, we utilized the highly self-incompatible pollinium-pollination system of Asclepias syriaca (Common Milkweed) to quantify how insect visitors influenced male reproductive success measured as pollen removal, female reproductive success measured as pollen deposition, and self-pollination rate. We also determined how floral-display size impacts both visitor behavior and self-pollination rate.ResultsFour insect taxonomic orders visited A. syriaca: Coleoptera, Diptera, Hymenoptera, and Lepidoptera. We focused on three groups of visitor taxa within two orders (Hymenoptera and Lepidoptera) with sample sizes large enough for quantitative analysis: Apis mellifera (Western Honey Bee), Bombus spp. (bumble bees) and lepidopterans (butterflies and moths). Qualitatively, lepidopterans had the highest pollinator importance values, but the large variability in the lepidopteran data precluded meaningful interpretation of much of their behavior. The introduced A. mellifera was the most effective and most important diurnal pollinator with regard to both pollen removal and pollen deposition. However, when considering the self-incompatibility of A. syriaca, A. mellifera was not the most important pollinator because of its high self-pollination rate as compared to Bombus spp. Additionally, the rate of self-pollination increased more rapidly with the number of flowers per inflorescence in A. mellifera than in the native Bombus spp.ConclusionsApis mellifera's high rate of self-pollination may have significant negative effects on both male and female reproductive successes in A. syriaca, causing different selection on floral-display size than native pollinators.

Project description:PurposePrevious approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative spectral-domain (SD) OCT data to train a DL algorithm to quantify glaucomatous structural damage on optic disc photographs.DesignCross-sectional study.ParticipantsA total of 32 820 pairs of optic disc photographs and SD OCT retinal nerve fiber layer (RNFL) scans from 2312 eyes of 1198 participants.MethodsThe sample was divided randomly into validation plus training (80%) and test (20%) sets, with randomization performed at the patient level. A DL convolutional neural network was trained to assess optic disc photographs and predict SD OCT average RNFL thickness.Main outcome measuresThe DL algorithm performance was evaluated in the test sample by evaluating correlation and agreement between the predictions and actual SD OCT measurements. We also assessed the ability to discriminate eyes with glaucomatous visual field loss from healthy eyes with area under the receiver operating characteristic (ROC) curves.ResultsThe mean prediction of average RNFL thickness from all 6292 optic disc photographs in the test set was 83.3±14.5 μm, whereas the mean average RNFL thickness from all corresponding SD OCT scans was 82.5±16.8 μm (P = 0.164). There was a very strong correlation between predicted and observed RNFL thickness values (Pearson r = 0.832; R2 = 69.3%; P < 0.001), with mean absolute error of the predictions of 7.39 μm. The area under the ROC curves for discriminating glaucomatous from healthy eyes with the DL predictions and actual SD OCT average RNFL thickness measurements were 0.944 (95% confidence interval [CI], 0.912-0.966) and 0.940 (95% CI, 0.902-0.966), respectively (P = 0.724).ConclusionsWe introduced a novel DL approach to assess fundus photographs and provide quantitative information about the amount of neural damage that can be used to diagnose and stage glaucoma. In addition, training neural networks to predict SD OCT data objectively represents a new approach that overcomes limitations of human labeling and could be useful in other areas of ophthalmology.

Project description:BackgroundPreliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a more precise and reproducible means of measuring BPE than human readers and thus might improve the predictive performance of future cancer development.PurposeTo determine if algorithmically extracted imaging features of BPE on screening breast MRI in high-risk women are associated with subsequent development of cancer.Study typeCase-control study.PopulationIn all, 133 women at high risk for developing breast cancer; 46 of these patients developed breast cancer subsequently over a follow-up period of 2 years.Field strength/sequence5 T or 3.0 T T1 -weighted precontrast fat-saturated and nonfat-saturated sequences and postcontrast nonfat-saturated sequences.AssessmentAutomatic features of BPE were extracted with a computer algorithm. Subjective BPE scores from five breast radiologists (blinded to clinical outcomes) were also available.Statistical testsLeave-one-out crossvalidation for a multivariate logistic regression model developed using the automatic features and receiver operating characteristic (ROC) analysis were performed to calculate the area under the curve (AUC). Comparison of automatic features and subjective features was performed using a generalized regression model and the P-value was obtained. Odds ratios for automatic and subjective features were compared.ResultsThe multivariate model discriminated patients who developed cancer from the patients who did not, with an AUC of 0.70 (95% confidence interval: 0.60-0.79, P < 0.001). The imaging features remained independently predictive of subsequent development of cancer (P < 0.003) when compared with the subjective BPE assessment of the readers.Data conclusionAutomatically extracted BPE measurements may potentially be used to further stratify risk in patients undergoing high-risk screening MRI.Level of evidence3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2019;50:456-464.

Dataset Information

Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods.

Publications

Understanding the Environmental Background of an Invasive Plant Species (<i>Asclepias syriaca</i>) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure