Dataset Information

Passive water ascent in a tall, scalable synthetic tree.

ABSTRACT: The transpiration cycle in trees is powered by a negative water potential generated within the leaves, which pumps water up a dense array of xylem conduits. Synthetic trees can mimic this transpiration cycle, but have been confined to pumping water across a single microcapillary or microfluidic channels. Here, we fabricated tall synthetic trees where water ascends up an array of large diameter conduits, to enable transpiration at the same macroscopic scale as natural trees. An array of 19 tubes of millimetric diameter were embedded inside of a nanoporous ceramic disk on one end, while their free end was submerged in a water reservoir. After saturating the synthetic tree by boiling it underwater, water can flow continuously up the tubes even when the ceramic disk was elevated over 3?m above the reservoir. A theory is developed to reveal two distinct modes of transpiration: an evaporation-limited regime and a flow-limited regime.

SUBMITTER: Shi W

PROVIDER: S-EPMC6959229 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Passive water ascent in a tall, scalable synthetic tree.

Shi Weiwei W Dalrymple Richard M RM McKenny Collin J CJ Morrow David S DS Rashed Ziad T ZT Surinach Daniel A DA Boreyko Jonathan B JB

Scientific reports 20200114 1

The transpiration cycle in trees is powered by a negative water potential generated within the leaves, which pumps water up a dense array of xylem conduits. Synthetic trees can mimic this transpiration cycle, but have been confined to pumping water across a single microcapillary or microfluidic channels. Here, we fabricated tall synthetic trees where water ascends up an array of large diameter conduits, to enable transpiration at the same macroscopic scale as natural trees. An array of 19 tubes ...[more]

PMID: 31937824

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Project description:PurposeCurrent limitations in methodologies used throughout machine-learning to investigate feature importance in boosted tree modelling prevent the effective scaling to datasets with a large number of features, particularly when one is investigating both the magnitude and directionality of various features on the classification into a positive or negative class. This manuscript presents a novel methodology, "Hollow-tree Super" (HOTS), designed to resolve and visualize feature importance in boosted tree models involving a large number of features. Further, this methodology allows for accurate investigation of the directionality and magnitude various features have on classification and incorporates cross-validation to improve the accuracy and validity of the determined features of importance.MethodsUsing the Iris dataset, we first highlight the characteristics of HOTS by comparing it to other commonly used techniques for feature importance, including Gini Importance, Partial Dependence Plots, and Permutation Importance, and explain how HOTS resolves the weaknesses present in these three strategies for investigating feature importance. We then demonstrate how HOTS can be utilized in high dimensional spaces such as neuroscientific setting, by taking 60 Schizophrenic subjects from the publicly available SchizConnect database and applying the method to determine which regions of the brain were most important for the positive and negative classification of schizophrenia as determined by the positive and negative syndrome scale (PANSS).ResultsHOTS effectively replicated and supported the findings of feature importance for classification of the Iris dataset when compared to Gini importance, Partial Dependence Plots and Permutation importance, determining 'petal length' as the most important feature for positive and negative classification. When applied to the Schizconnect dataset, HOTS was able to resolve from 379 independent features, the top 10 most important features for classification, as well as their directionality for classification and magnitude compared to other features. Cross-validation supported that these same 10 features were consistently used in the decision-making process across multiple trees, and these features were localised primarily to the occipital and parietal cortices, commonly disturbed brain regions in those afflicted with Schizophrenia.ConclusionHOTS effectively overcomes previous challenges of identifying feature importance at scale, and can be utilized across a swathe of disciplines. As computational power and data quantity continues to expand, it is imperative that a methodology is developed that is able to handle the demands of working with large datasets that contain a large number of features. This approach represents a unique way to investigate both the directionality and magnitude of feature importance when working at scale within a boosted tree model that can be easily visualized within commonly used software.

Dataset Information

Passive water ascent in a tall, scalable synthetic tree.

Publications

Passive water ascent in a tall, scalable synthetic tree.

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