Quantitative image analysis as a tool for Yarrowia lipolytica dimorphic growth evaluation in different culture media

Yarrowia lipolytica, a yeast strain with a huge biotechnological potential, capable to produce metabolites such as γ-decalactone, citric acid, intracellular lipids and enzymes, possesses the ability to change its morphology in response to environmental conditions. In the present study, a quantitative image analysis (QIA) procedure was developed for the identification and quantification of Y. lipolytica W29 and MTLY40-2P strains dimorphic growth, cultivated in batch cultures on hydrophilic (glucose and N-acetylglucosamine (GlcNAc) and hydrophobic (olive oil and castor oil) media. The morphological characterization of yeast cells by QIA techniques revealed that hydrophobic carbon sources, namely castor oil, should be preferred for both strains growth in the yeast single cell morphotype. On the other hand, hydrophilic sugars, namely glucose and GlcNAc caused a dimorphic transition growth towards the hyphae morphotype. Experiments for γ-decalactone production with MTLY40-2P strain in two distinct morphotypes (yeast single cells and hyphae cells) were also performed. The obtained results showed the adequacy of the proposed morphology monitoring tool in relation to each morphotype on the aroma production ability. The present work allowed establishing that QIA techniques can be a valuable tool for the identification of the best culture conditions for industrial processes implementation.

Light-distortion analysis as a possible indicator of visual quality after refractive lens exchange with diffractive multifocal intraocular lenses

METHODS: Refractive lens exchange was performed with implantation of an AT Lisa 839M (trifocal) or 909MP (bifocal toric) IOL, the latter if corneal astigmatism was more than 0.75 diopter (D). The postoperative visual and refractive outcomes were evaluated. A prototype light-distortion analyzer was used to quantify the postoperative light-distortion indices. A control group of eyes in which a Tecnis ZCB00 1-piece monofocal IOL was implanted had the same examinations. RESULTS: A trifocal or bifocal toric IOL was implanted in 66 eyes. The control IOL was implanted in 18 eyes. All 3 groups obtained a significant improvement in uncorrected distance visual acuity (UDVA) (P < .001) and corrected distance visual acuity (CDVA) (P Z .001). The mean uncorrected near visual acuity (UNVA) was 0.123 logMAR with the trifocal IOL and 0.130 logMAR with the bifocal toric IOL. The residual refractive cylinder was less than 1.00 D in 86.7% of cases with the toric IOL. The mean light-distortion index was significantly higher in the multifocal IOL groups than in the monofocal group (P < .001), although no correlation was found between the light-distortion index and CDVA. CONCLUSIONS: The multifocal IOLs provided excellent UDVA and functional UNVA despite increased light-distortion indices. The light-distortion analyzer reliably quantified a subjective component of vision distinct from visual acuity; it may become a useful adjunct in the evaluation of visual quality obtained with multifocal IOLs.

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.