Dataset Information

Hierarchical classification of snowmelt episodes in the Pyrenees using seismic data.

ABSTRACT: In recent years the analysis of the variations of seismic background signal recorded in temporal deployments of seismic stations near river channels has proved to be a useful tool to monitor river flow, even for modest discharges. The objective of this work is to apply seismic methods to the characterization of the snowmelt process in the Pyrenees, by developing an innovative approach based on the hierarchical classification of the daily spectrograms. The CANF seismic broad-band station, part of the Geodyn facility in the Laboratorio Subterráneo de Canfranc (LSC), is located in an underground tunnel in the Central Pyrenees, at about 400 m of the Aragón River channel, hence providing an excellent opportunity to explore the possibilities of the seismic monitoring of hydrological events at long term scale. We focus here on the identification and analysis of seismic signals generated by variations in river discharge due to snow melting during a period of six years (2011-2016). During snowmelt episodes, the temporal variations of the discharge at the drainage river result in seismic signals with specific characteristics allowing their discrimination from other sources of background vibrations. We have developed a methodology that use seismic data to monitor the time occurrence and properties of the thawing stages. The proposed method is based on the use of hierarchical clustering techniques to classify the daily seismic spectra according to their similarity. This allows us to discriminate up to four different types of episodes, evidencing changes in the duration and intensity of the melting process which in turn depends on variations in the meteorological and hydrological conditions. The analysis of six years of continuous seismic data from this innovative procedure shows that seismic data can be used to monitor snowmelt on long-term time scale and hence contribute to climate change studies.

SUBMITTER: Diaz J

PROVIDER: S-EPMC6786603 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Hierarchical classification of snowmelt episodes in the Pyrenees using seismic data.

Díaz Jordi J Sánchez-Pastor Pilar P Ruiz Mario M

PloS one 20191010 10

In recent years the analysis of the variations of seismic background signal recorded in temporal deployments of seismic stations near river channels has proved to be a useful tool to monitor river flow, even for modest discharges. The objective of this work is to apply seismic methods to the characterization of the snowmelt process in the Pyrenees, by developing an innovative approach based on the hierarchical classification of the daily spectrograms. The CANF seismic broad-band station, part of ...[more]

PMID: 31600292

Similar Datasets

Project description:Single-cell RNA sequencing (scRNA-seq) is an invaluable tool for profiling cells in complex tissues and dissecting activation states that lack well-defined surface protein expression. For immune cells, the transcriptomic profile captured by scRNA- seq cannot always identify cell states and subsets defined by conventional flow cytometry. Emerging technologies have enabled multimodal sequencing of single cells, such as paired sequencing of the transcriptome and surface proteome by CITE-seq, but integrating these high dimensional modalities for accurate cell type annotation remains a challenge in the field. Here, we describe a machine learning tool called MultiModal Classifier Hierarchy (MMoCHi) for the cell-type annotation of CITE-seq data. Our classifier involves several steps: 1) we use landmark registration to remove batch-related staining artifacts in CITE-Seq protein expression, 2) the user defines a hierarchy of classifications based on cell type similarity and ontology and provides markers (protein or gene expression) for the identification of ground truth populations within the dataset by threshold gating, 3) progressing through this user-defined hierarchy, we train a random forest classifier using all available modalities (surface proteome and transcriptome data), and 4) we use these forests to predict cell types across the entire dataset. Applying MMoCHi to CITE-seq data of immune cells isolated from eight distinct tissue sites of two human organ donors yields high-purity cell type annotations encompassing the broad array of immune cell states in the dataset. This includes T and B cell memory subsets, macrophages and monocytes, and natural killer cells, as well as rare populations of plasmacytoid dendritic cells, innate T cells, and innate lymphoid cell subsets. We validate the use of feature importances extracted from the classifier hierarchy to select robust genes for improved identification of T cell memory subsets by scRNA-seq. Together, MMoCHi provides a comprehensive system of tools for the batch-correction and cell- type annotation of CITE-seq data. Moreover, this tool provides flexibility in classification hierarchy design allowing for cell type annotations to reflect a researcher’s specific experimental design. This flexibility also renders MMoCHi readily extendable beyond immune cell annotation, and potentially adaptable to other sequencing modalities.

Project description:Microbial activity is highly dependent on climatic factors (moisture and temperature) and edaphic characteristics in temperate ecosystems. Moreover, soil microbial community composition in high mountain areas is less known when compared to plant communities. In this study we investigated the soil microbial community from a functional perspective using PLFA (phospholipid fatty acid) methods in the four aspects of four summits (2,242 - 3,012 m above sea level) in the Spanish Central Pyrenees. Soil organic carbon (C), microbial biomass and nutrient dynamics ([Formula: see text] + [Formula: see text], N mineralization and nitrification potential) were also determined. Microbial biomass C was highest in the lowermost summit and decreased by approximately 50, 14 and 12% with increasing altitude. In each summit soil [Formula: see text] and [Formula: see text] concentrations differed significantly among summits and aspects. Soil nitrification potential varied significantly between the factors summit and aspects, e.g., southerly vs. northerly, easterly vs. westerly aspects. Gram negative bacteria and Actinobacteria functional groups dominated the microbial community, with almost 40% of the total PLFA. Non-metric multidimensional scale (NMS) analysis showed that most of the PLFA functional groups were present in all summits and aspects, although with specific biomarkers. A high abundance of biomarkers 16:1ω9c and 16:0 2OH (gram negative bacteria) were obtained in the lowermost summit, while the biomarkers 16.1ω7cDMA (anaerobes) and 19:3ω6c (Eukaryote) were only found in the uppermost summit. Linear mixed model (lmm) analysis was used with summit as fixed effect and aspect as random effect. In general, our results demonstrate a fundamental role for environment, principally moisture, temperature and organic matter in explaining the pattern observed for soil PLFA biomarkers. Under a global change scenario, we need to shed light on the relationships between soil microbial functional groups and soil nutrient-related variables in order to identify the associated patterns of decomposition rates and soil processes driven by microbial communities in mountain areas. The results could thus be used in global predictive models on climate change impact on C or N cycles in these environments.

Dataset Information

Hierarchical classification of snowmelt episodes in the Pyrenees using seismic data.

Publications

Hierarchical classification of snowmelt episodes in the Pyrenees using seismic data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets