Dataset Information

A field-to-desktop toolchain for X-ray CT densitometry enables tree ring analysis.

ABSTRACT:

Background and aims

Disentangling tree growth requires more than ring width data only. Densitometry is considered a valuable proxy, yet laborious wood sample preparation and lack of dedicated software limit the widespread use of density profiling for tree ring analysis. An X-ray computed tomography-based toolchain of tree increment cores is presented, which results in profile data sets suitable for visual exploration as well as density-based pattern matching.

Methods

Two temperate (Quercus petraea, Fagus sylvatica) and one tropical species (Terminalia superba) were used for density profiling using an X-ray computed tomography facility with custom-made sample holders and dedicated processing software.

Key results

Density-based pattern matching is developed and able to detect anomalies in ring series that can be corrected via interactive software.

Conclusions

A digital workflow allows generation of structure-corrected profiles of large sets of cores in a short time span that provide sufficient intra-annual density information for tree ring analysis. Furthermore, visual exploration of such data sets is of high value. The dated profiles can be used for high-resolution chronologies and also offer opportunities for fast screening of lesser studied tropical tree species.

SUBMITTER: De Mil T

PROVIDER: S-EPMC4904179 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A field-to-desktop toolchain for X-ray CT densitometry enables tree ring analysis.

De Mil Tom T Vannoppen Astrid A Beeckman Hans H Van Acker Joris J Van den Bulcke Jan J

Annals of botany 20160423 7

<h4>Background and aims</h4>Disentangling tree growth requires more than ring width data only. Densitometry is considered a valuable proxy, yet laborious wood sample preparation and lack of dedicated software limit the widespread use of density profiling for tree ring analysis. An X-ray computed tomography-based toolchain of tree increment cores is presented, which results in profile data sets suitable for visual exploration as well as density-based pattern matching.<h4>Methods</h4>Two temperate ...[more]

PMID: 27107414

Similar Datasets

Project description:BackgroundThe hydraulic conductivity of the stem is a major factor limiting the capability of trees to transport water from the soil to transpiring leaves. During drought conditions, the conducting capacity of xylem can be reduced by some conduits being filled with gas, i.e. embolized. In order to understand the dynamics of embolism formation and repair, considerable attention has been given to developing reliable and accurate methods for quantifying the phenomenon. In the past decade, non-destructive imaging of embolism formation in living plants has become possible. Magnetic resonance imaging has been used to visualize the distribution of water within the stem, but in most cases it is not possible to resolve individual cells. Recently, high-resolution synchrotron x-ray microtomography has been introduced as a tool to visualize the water contents of individual cells in vivo, providing unprecedented insight into the dynamics of embolism repair. We have investigated the potential of an x-ray tube -based microtomography setup to visualize and quantify xylem embolism and embolism repair in water-stressed young saplings and shoot tips of Silver and Curly birch (Betula pendula and B. pendula var. carelica).ResultsFrom the microtomography images, the water-filled versus gas-filled status of individual xylem conduits can be seen, and the proportion of stem cross-section that consists of embolized tissue can be calculated. Measuring the number of embolized vessels in the imaged area is a simple counting experiment. In the samples investigated, wood fibers were cavitated in a large proportion of the xylem cross-section shortly after watering of the plant was stopped, but the number of embolized vessels remained low several days into a drought period. Under conditions of low evaporative demand, also refilling of previously embolized conduits was observed.ConclusionsDesktop x-ray microtomography is shown to be an effective method for evaluating the water-filled versus embolized status of the stem xylem in a small living sapling. Due to its non-destructive nature, the risk of inducing embolisms during sampling is greatly reduced. Compared with synchrotron imaging beamlines, desktop microtomography offers easier accessibility, while maintaining sufficient resolution to visualize the water contents of individual cells.

Project description:PurposeTo train and validate a predictive model of mortality for hospitalized COVID-19 patients based on lung densitometry.MethodsTwo-hundred-fifty-one patients with respiratory symptoms underwent CT few days after hospitalization. "Aerated" (AV), "consolidated" (CV) and "intermediate" (IV) lung sub-volumes were quantified by an operator-independent method based on individual HU maximum gradient recognition. AV, CV, IV, CV/AV, IV/AV, and HU of the first peak position were extracted. Relevant clinical parameters were prospectively collected. The population was composed by training (n = 166) and validation (n = 85) consecutive cohorts, and backward multi-variate logistic regression was applied on the training group to build a CT_model. Similarly, models including only clinical parameters (CLIN_model) and both CT/clinical parameters (COMB_model) were developed. Model's performances were assessed by goodness-of-fit (H&L-test), calibration and discrimination. Model's performances were tested in the validation group.ResultsForty-three patients died (25/18 in training/validation). CT_model included AVmax (i.e. maximum AV between lungs), CV and CV/AE, while CLIN_model included random glycemia, C-reactive protein and biological drugs (protective). Goodness-of-fit and discrimination were similar (H&L:0.70 vs 0.80; AUC:0.80 vs 0.80). COMB_model including AVmax, CV, CV/AE, random glycemia, biological drugs and active cancer, outperformed both models (H&L:0.91; AUC:0.89, 95%CI:0.82-0.93). All models showed good calibration (R2:0.77-0.97). Despite several patient's characteristics were different between training and validation cohorts, performances in the validation cohort confirmed good calibration (R2:0-70-0.81) and discrimination for CT_model/COMB_model (AUC:0.72/0.76), while CLIN_model performed worse (AUC:0.64).ConclusionsFew automatically extracted densitometry parameters with clear functional meaning predicted mortality of COVID-19 patients. Combined with clinical features, the resulting predictive model showed higher discrimination/calibration.

Dataset Information

A field-to-desktop toolchain for X-ray CT densitometry enables tree ring analysis.

Background and aims

Methods

Key results

Conclusions

Publications

A field-to-desktop toolchain for X-ray CT densitometry enables tree ring analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets