An integrative bioinformatics pipeline shows that honeybee-associated microbiomes are driven primarily by pollen composition

The microbiomes associated with bee nests influence colony health through various mechanisms,although it is not yet clear how honeybee congeners differ in microbiome assembly processes,in particular the degrees to which floral visitations and the environment contribute to different aspects of diversity.We used DNA metabarcoding to sequence bacterial 16S rRNA from honey and stored pollen from nests of 4 honeybee species(Apis cerana,A.dorsata,A.florea,and A.laboriosa)sampled throughout Yunnan,China,a global biodiversity hotspot.We developed a computational pipeline integrating multiple databases for quantifying key facets of diversity,including compositional,taxonomic,phylogenetic,and functional ones.Further,we assessed candidate drivers of observed microbiome dissimilarity,particularly differences in floral visitations,habitat disturbance,and other key environmental variables.Analyses revealed that microbiome alpha diversity was broadly equivalent across the study sites and between bee species,apart from functional diversity which was very low in nests of the reclusive A.laboriosa.Turnover in microbiome composition across Yunnan was driven predominantly by pollen composition.Human disturbance negatively impacted both compositional and phylogenetic alpha diversity of nest microbiomes,but did not correlate with microbial turnover.We herein make progress in understanding microbiome diversity associated with key pollinators in a biodiversity hotspot,and provide a model for the use of a comprehensive informatics framework in assessing pattern and drivers of diversity,which enables the inclusion of explanatory variables both subtly and fundamentally different and enables elucidation of emergent or unexpected drivers.

Near-infrared spectroscopy and chemometric modelling for rapid diagnosis of kidney disease

Rapid diagnosis is important for efficient treatment in clinical medicine.This study aimed at development of a method for rapid and reliable diagnosis using near-infrared(NIR)spectra of human serum samples with the help of chemometric modelling.The NIR spectra of sera from 48 healthy individuals and 16 patients with suspected kidney disease were analyzed.Discrete wavelet transform(DWT)and variable selection were adopted to extract the useful information from the spectra.Principal component analysis(PCA),linear discriminant analysis(LDA)and partial least squares discriminant analysis(PLSDA)were used for discrimination of the samples.Classification of the two-class sera was obtained using LDA and PLSDA with the help of DWT and variable selection.DWT-LDA produced 93.8%and 83.3%of the recognition rates for the validation samples of the two classes,and 100%recognition rates were obtained using DWT-PLSDA.The results demonstrated that the tiny differences between the spectra of the sera were effectively explored using DWT and variable selection,and the differences can be used for discrimination of the sera from healthy and possible patients.NIR spectroscopy and chemometrics may be a potential technique for fast diagnosis of kidney disease.

RhizoVision Crown:An Integrated Hardware and Software Platform for Root Crown Phenotyping

Root crown phenotyping measures the top portion of crop root systems and can be used for marker-assisted breeding,genetic mapping,and understanding how roots influence soil resource acquisition.Several imaging protocols and image analysis programs exist,but they are not optimized for high-throughput,repeatable,and robust root crown phenotyping.The RhizoVision Crown platform integrates an imaging unit,image capture software,and image analysis software that are optimized for reliable extraction of measurements from large numbers of root crowns.The hardware platform utilizes a backlight and a monochrome machine vision camera to capture root crown silhouettes.The RhizoVision Imager and RhizoVision Analyzer are free,open-source software that streamline image capture and image analysis with intuitive graphical user interfaces.The RhizoVision Analyzer was physically validated using copper wire,and features were extensively validated using 10,464 groundtruth simulated images of dicot and monocot root systems.This platform was then used to phenotype soybean and wheat root crowns.A total of 2,799 soybean(Glycine max)root crowns of 187 lines and 1,753 wheat(Triticum aestivum)root crowns of 186 lines were phenotyped.Principal component analysis indicated similar correlations among features in both species.The maximum heritability was 0.74 in soybean and 0.22 in wheat,indicating that differences in species and populations need to be considered.The integrated RhizoVision Crown platform facilitates high-throughput phenotyping of crop root crowns and sets a standard by which open plant phenotyping platforms can be benchmarked.