Dataset Information

Predicting osmotic potential from measurements of refractive index in cherries, grapes and plums.

ABSTRACT: Studies of fruit tree water relations often require data on water potentials of fruit. However, this is sometimes difficult because the fruit stalks are not sufficiently long for use in a pressure bomb. Also, because fruit xylem function is often lost during maturation. In the absence of significant turgor, the osmotic potential of the expressed juice is a useful proxy for a fruit's water potential. The osmotic potential of most fleshy fruit is determined largely by the concentration of soluble carbohydrates and this can be quantified by osmometry. Soluble solids may also be quantified by refractometry. Compared with osmometry, refractometry is markedly less expensive and also much faster. Hence, it is better suited to high-throughput analyses. The objective of this study was to establish relationships between the osmotic potentials of juices expressed from sweet cherries and sour cherries, grapes and plums as determined using a vapor pressure osmometer and their soluble solids concentrations as determined using a refractometer. The data reveal close relationships within all these species. Except for plums, the relationships between species were almost identical. This is due to similarity among cultivars and species in the relative abundances of the same set of major osmolytes-i.e. the carbohydrates glucose, fructose and sorbitol and the potassium salts of the organic acids malate or tartrate. For plums, the relationship between osmotic potential and soluble solids concentration was slightly displaced. Our findings indicate osmotic potentials may be reliably predicted from soluble solids concentrations determined by refractometry.

SUBMITTER: Winkler A

PROVIDER: S-EPMC6239309 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Predicting osmotic potential from measurements of refractive index in cherries, grapes and plums.

Winkler Andreas A Knoche Moritz M

PloS one 20181116 11

Studies of fruit tree water relations often require data on water potentials of fruit. However, this is sometimes difficult because the fruit stalks are not sufficiently long for use in a pressure bomb. Also, because fruit xylem function is often lost during maturation. In the absence of significant turgor, the osmotic potential of the expressed juice is a useful proxy for a fruit's water potential. The osmotic potential of most fleshy fruit is determined largely by the concentration of soluble ...[more]

PMID: 30444908

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Project description:BackgroundRefractive surgery research aims to optimally precategorize patients by their suitability for various types of surgery. Recent advances have led to the development of artificial intelligence-powered algorithms, including machine learning approaches, to assess risks and enhance workflow. Large language models (LLMs) like ChatGPT-4 (OpenAI LP) have emerged as potential general artificial intelligence tools that can assist across various disciplines, possibly including refractive surgery decision-making. However, their actual capabilities in precategorizing refractive surgery patients based on real-world parameters remain unexplored.ObjectiveThis exploratory study aimed to validate ChatGPT-4's capabilities in precategorizing refractive surgery patients based on commonly used clinical parameters. The goal was to assess whether ChatGPT-4's performance when categorizing batch inputs is comparable to those made by a refractive surgeon. A simple binary set of categories (patient suitable for laser refractive surgery or not) as well as a more detailed set were compared.MethodsData from 100 consecutive patients from a refractive clinic were anonymized and analyzed. Parameters included age, sex, manifest refraction, visual acuity, and various corneal measurements and indices from Scheimpflug imaging. This study compared ChatGPT-4's performance with a clinician's categorizations using Cohen κ coefficient, a chi-square test, a confusion matrix, accuracy, precision, recall, F1-score, and receiver operating characteristic area under the curve.ResultsA statistically significant noncoincidental accordance was found between ChatGPT-4 and the clinician's categorizations with a Cohen κ coefficient of 0.399 for 6 categories (95% CI 0.256-0.537) and 0.610 for binary categorization (95% CI 0.372-0.792). The model showed temporal instability and response variability, however. The chi-square test on 6 categories indicated an association between the 2 raters' distributions (χ²5=94.7, P<.001). Here, the accuracy was 0.68, precision 0.75, recall 0.68, and F1-score 0.70. For 2 categories, the accuracy was 0.88, precision 0.88, recall 0.88, F1-score 0.88, and area under the curve 0.79.ConclusionsThis study revealed that ChatGPT-4 exhibits potential as a precategorization tool in refractive surgery, showing promising agreement with clinician categorizations. However, its main limitations include, among others, dependency on solely one human rater, small sample size, the instability and variability of ChatGPT's (OpenAI LP) output between iterations and nontransparency of the underlying models. The results encourage further exploration into the application of LLMs like ChatGPT-4 in health care, particularly in decision-making processes that require understanding vast clinical data. Future research should focus on defining the model's accuracy with prompt and vignette standardization, detecting confounding factors, and comparing to other versions of ChatGPT-4 and other LLMs to pave the way for larger-scale validation and real-world implementation.

Dataset Information

Predicting osmotic potential from measurements of refractive index in cherries, grapes and plums.

Publications

Predicting osmotic potential from measurements of refractive index in cherries, grapes and plums.

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