Automatic Growth Detection of Cell Cultures through Outlier Techniques using 2D Images

Using conventional statistics, we have developed a new method for cell culture analysis through outlier detection techniques. Statistical methods enable researchers in microbiology to identify experimental parameters that are critical for colony growth and inhibition. This paper reports a method for analysing 2D images of cell cultures in Petri dishs, such as fungi, bacteria or yeast. The aim of this study was to obtain a sensitive and robust method for detection of growth rate, surface coverage and the approximate number of cells in the colony. For testing we have implemented a software application called MoldATRIX. This software generates useful statistics and displays critical information about the cell colony area. Our results were obtained by analyzing a series of digital images of Aspergillus niger cultures at different time intervals. Moreover, our results show the behavior of Aspergillus niger on leather.

Structural Properties of Gene Promoters Highlight More than Two Phenotypes of Diabetes

<div><p>Genome-wide association studies (GWAS) published in the last decade raised the number of loci associated with type 1 (T1D) and type 2 diabetes (T2D) to more than 50 for each of these diabetes phenotypes. The environmental factors seem to play an important role in the expression of these genes, acting through transcription factors that bind to promoters. Using the available databases we examined the promoters of various genes classically associated with the two main diabetes phenotypes. Our comparative analyses have revealed significant architectural differences between promoters of genes classically associated with T1D and T2D. Nevertheless, five gene promoters (about 16%) belonging to T1D and six gene promoters (over 19%) belonging to T2D have shown some intermediary structural properties, suggesting a direct relationship to either LADA (Latent Autoimmune Diabetes in Adults) phenotype or to non-autoimmune type 1 phenotype. The distribution of these promoters in at least three separate classes seems to indicate specific pathogenic pathways. The image-based patterns (DNA patterns) generated by promoters of genes associated with these three phenotypes support the clinical observation of a smooth link between specific cases of typical T1D and T2D. In addition, a global distribution of these DNA patterns suggests that promoters of genes associated with T1D appear to be evolutionary more conserved than those associated with T2D. Though, the image based patterns obtained by our method might be a new useful parameter for understanding the pathogenetic mechanism and the diabetogenic gene networks.</p></div

Schematic overview of the promoter analysis.

<p>(A) promoter sequences, (B) Kappa IC and (C+G)% values are extracted from each sliding window, (C) sliding window values plotted on a graph which shows a recognizable image-based pattern for each promoter sequence, (D) the center of weight of each promoter pattern plotted on a second graph in order to show the distribution of 8,515 promoters. Red color areas represent denser clusters of promoters. (E) The representative eukaryotic promoter classes are shown in the following sections: AT-based class, CG-based class, ATCG-compact class, ATCG-balanced class, ATCG-middle class, ATCG-less class, AT-less class, CG-spike class, CG-less class and ATspike class [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137950#pone.0137950.ref057" target="_blank">57</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0137950#pone.0137950.ref059" target="_blank">59</a>].</p

Image-based promoter patterns of genes classically associated with T1D and T2D.

<p>(A-O) promoters of genes associated with T1D, (P-AD) promoters of genes associated with T2D. Each black circle represents the center of weight of the promoter pattern.</p

Redristribution of promoters according to T1D, IDM and T2D.

<p>(A) T1D promoters (blue dots), (B) T2D promoters (red dots) and (C) promoters from genes associated with the “intermediary” phenotype (green dots). (D) Full separation of T1D and T2D after the inclusion of the intermediary phenotype, (E) C+G values of intermediary phenotype areas overlapping equally in both T1D and T2D regions, (F) Kappa IC values of intermediary phenotype overlapping only with T2D areas.</p

Distribution of T1D and T2D gene promoters.

<p>(A) overlapping distribution of T1D and T2D gene promoters on a genome-wide distribution of 8,515 Homo sapiens promoters, (B) mean distribution of T1D and T2D gene promoters, (C) distribution of T1D gene promoters, (D) distribution of T2D gene promoters. Each circle represents the center of weight from a promoter pattern and the circle color is associated with a corresponding gene promoter.</p

General distribution of the three phenotypes.

<p>(A) Complete separation of T1D and T2D after the inclusion of the intermediary phenotype and (B) the global distribution of the three phenotypes.</p

Markers of Oxidative Stress and Antioxidant Defense in Romanian Patients with Type 2 Diabetes Mellitus and Obesity

Type 2 diabetes mellitus (T2DM) is strongly associated with obesity. The adipose tissue secretes bioactive adipokines leading to low grade inflammation, amplified by oxidative stress, which promotes the formation of advanced glycation end products and eventually leads to dyslipidemia and vascular complications. The aim of this study was to correlate anthropometric, biochemical and oxidative stress parameters in newly diagnosed (ND) T2DM patients and to investigate the role of oxidative stress in T2DM associated with obesity. A group of 115 ND- T2DM patients was compared to a group of 32 healthy subjects in terms of clinical, anthropometric, biochemical and oxidative stress parameters. ND-T2DM patients had significantly lower adiponectin, glutathione (GSH) and gluthatione peroxidase (GPx) and elevated insulin, proinsulin, HOMA-IR index, proinsulin/insulin (P/I) and proinsulin/adiponectin (P/A) ratio, fructosamine, and total oxidant status (TOS). The total body fat mass was positively correlated with total oxidant status (TOS). Positive correlations were found between TOS and glycated hemoglobin (HbA1c), and between TOS and glycaemia. Negative correlations were identified between: GPx and glycaemia, GPx and HbA1c, and also between GSH and fructosamine. The total antioxidant status was negatively correlated with the respiratory burst. The identified correlations suggest the existence of a complex interplay between diabetes, obesity and oxidative stress