Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

BackgroundThere is still an ongoing discussion on the prognostic value of cystatin C in assessment of kidney function. Accordingly, the present study aimed to conduct a meta-analysis to provide evidence for the prognostic value of this biomarker for acute kidney injury (AKI) in children.MethodsAn extensive search was performed in electronic databases of Medline, Embase, ISI Web of Science, Cochrane library and Scopus until the end of 2015. Standardized mean difference (SMD) with a 95% of confidence interval (95% CI) and the prognostic performance characteristics of cystatin C in prediction of AKI were assessed. Analyses were stratified based on the sample in which the level of cystatin C was measured (serum vs. urine).ResultsA total of 24 articles were included in the meta-analysis [1948 children (1302 non-AKI children and 645 AKI cases)]. Serum (SMD = 0.96; 95% CI: 0.68-1.24; p < 0.0001) and urine (SMD = 0.54; 95% CI:0.34-0.75; p < 0.0001) levels of cystatin C were significantly higher in children with AKI. Overall area under the curve of serum cystatin C and urine cystatin C in prediction of AKI were 0.83 (95% CI: 0.80-0.86) and 0.85 (95% CI: 0.81-0.88), respectively. The best sensitivity (value = 0.85; 95% CI: 0.78-0.90) and specificity (value = 0.61; 95% CI: 0.48-0.73), were observed for the serum concentration of this protein and in the cut-off points between 0.4-1.0 mg/L.ConclusionThe findings of the present study showed that cystatin C has an acceptable prognostic value for prediction of AKI in children. Since the serum level of cystatin C rises within the first 24 h of admission in patients with AKI, this biomarker can be a suitable alternative for traditional diagnostic measures.

Our results identify that the sensitivity of serum CysC for detecting AKI is higher than that of serum Cr in a heterogeneous pediatric intensive care unit (PICU) population.

No abstract

No abstract