Effect of storage conditions on seed germination of eigTyrrhenian endemic vascular plant species of conservation interest

The conservation of endemic and endangered plant species is of great interest to the scientific and research community. In this frame, seed banks play a crucial role when biodiversity preservation and climate change are considered. The study of seed viability and germination during storage conditions provides basic and useful information to ensure successful ex situ conservation. The aim of this study was to evaluate whether storage time and conditions (i.e., base collection at -25°C and active collection at +5°C) affect seed germination in the long term. For these purposes, eight Tyrrhenian endemic vascular plant species (mostly endangered) with orthodox seeds were studied: Brassica insularis, Centranthus amazonum, Dianthus morisianus, Digitalis purpurea var. gyspergerae, Ferula arrigonii, Helicodiceros muscivorus, Iberis integerrima and Verbascum plantagineum. These species were stored in the Sardinian Germplasm Bank (BG-SAR) at -25°C and at +5°C for a time ranging from 2 to 12 years. Germination tests were carried out following the optimal conditions reported in the literature for each species. The results showed, in general terms, the high seed germination capacity of all species stored at both conditions; regarding the time of seed storage, germination in some tested species (such as B. insularis and C. amazonum) slightly decreased over time. We argued that seed dehydration, low seed moisture content during storage and the use of hermetic glass containers can be considered key factors for long-term conservation of these orthodox seeds. In conclusion, this study showed that the conservation of these endemic species is ensured by seed bank storage, according to the general assumption that seed longevity depends on seed lot quality, on well-sealed storage containers and conditions before and during storage

Efficacy of Non-Pharmacological Interventions to Prevent and Treat Delirium in Older Patients : A Systematic Overview. The SENATOR project ONTOP Series

The research leading to these results has received funding from the European Union Seventh Framework program (FP7/2007-2013) under grant agreement n° 305930 (SENATOR). The funders had no role in the study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.Peer reviewedPublisher PD

Arginine metabolism in Trichomonas vaginalis infected with Mycoplasma hominis

Both Mycoplasma hominis and Trichomonas vaginalis utilize arginine as an energy source via the arginine dihydrolase (ADH) pathway. It has been previously demonstrated that M. hominis forms a stable intracellular relationship with T. vaginalis; hence, in this study we examined the interaction of two localized ADH pathways by comparing T. vaginalis strain SS22 with the laboratory-generated T. vaginalis strain SS22-MOZ2 infected with M. hominis MOZ2. The presence of M. hominis resulted in an approximately 16-fold increase in intracellular ornithine and a threefold increase in putrescine, compared with control T. vaginalis cultures. No change in the activity of enzymes of the ADH pathway could be demonstrated in SS22-MOZ2 compared with the parent SS22, and the increased production of ornithine could be attributed to the presence of M. hominis. Using metabolic flow analysis it was determined that the elasticity of enzymes of the ADH pathway in SS22-MOZ2 was unchanged compared with the parent SS22; however, the elasticity of ornithine decarboxylase (ODC) in SS22 was small, and it was doubled in SS22-MOZ2 cells. The potential benefit of this relationship to both T. vaginalis and M. hominis is discussed

Delayed diagnosis of coeliac disease increases cancer risk

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Accumulation of neutral lipids in peripheral blood mononuclear cells as a distinctive trait of Alzheimer patients and asymptomatic subjects at risk of disease

<p>Abstract</p> <p>Background</p> <p>Alzheimer's disease is the most common progressive neurodegenerative disease. In recent years, numerous progresses in the discovery of novel Alzheimer's disease molecular biomarkers in brain as well as in biological fluids have been made. Among them, those involving lipid metabolism are emerging as potential candidates. In particular, an accumulation of neutral lipids was recently found by us in skin fibroblasts from Alzheimer's disease patients. Therefore, with the aim to assess whether peripheral alterations in cholesterol homeostasis might be relevant in Alzheimer's disease development and progression, in the present study we analyzed lipid metabolism in plasma and peripheral blood mononuclear cells from Alzheimer's disease patients and from their first-degree relatives.</p> <p>Methods</p> <p>Blood samples were obtained from 93 patients with probable Alzheimer's disease and from 91 of their first-degree relatives. As controls we utilized 57, cognitively normal, over-65 year-old volunteers and 113 blood donors aged 21-66 years, respectively. Data are reported as mean ± standard error. Statistical calculations were performed using the statistical analysis software Origin 8.0 version. Data analysis was done using the Student t-test and the Pearson test.</p> <p>Results</p> <p>Data reported here show high neutral lipid levels and increased ACAT-1 protein in about 85% of peripheral blood mononuclear cells freshly isolated (<it>ex vivo</it>) from patients with probable sporadic Alzheimer's disease compared to about 7% of cognitively normal age-matched controls. A significant reduction in high density lipoprotein-cholesterol levels in plasma from Alzheimer's disease blood samples was also observed. Additionally, correlation analyses reveal a negative correlation between high density lipoprotein-cholesterol and cognitive capacity, as determined by Mini Mental State Examination, as well as between high density lipoprotein-cholesterol and neutral lipid accumulation. We observed great variability in the neutral lipid-peripheral blood mononuclear cells data and in plasma lipid analysis of the subjects enrolled as Alzheimer's disease-first-degree relatives. However, about 30% of them tend to display a peripheral metabolic cholesterol pattern similar to that exhibited by Alzheimer's disease patients.</p> <p>Conclusion</p> <p>We suggest that neutral lipid-peripheral blood mononuclear cells and plasma high density lipoprotein-cholesterol determinations might be of interest to outline a distinctive metabolic profile applying to both Alzheimer's disease patients and asymptomatic subjects at higher risk of disease.</p

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses

URBAN LIVEABILITY AS RESULT OF DIFFERENT ASPECTS

Nowadays it is possible to identify a series of parameters that contribute to defining the liveability characteristics of a public space. It is important that all the parameters are satisfied because they are elements that interfere with each other. Morphological characteristics, which partly contribute to defining the environmental performance of the space, together with the functional characteristics of the area, determined by the presence of activities, must be assessed together; however, evaluating them not only in qualitative but also in quantitative terms is not always easy to do. The paper presents a way to evaluate this characteristic of urban space through examples

A Filter-Based Evolutionary Approach for Selecting Features in High-Dimensional Micro-array Data

Evolutionary algorithms have received much attention in extracting knowledge on high-dimensional micro-array data, being crucial to their success a suitable definition of the search space of the potential solutions. In this paper, we present an evolutionary approach for selecting informative genes (features) to predict and diagnose cancer. We propose a procedure that combines results of filter methods, which are commonly used in the field of data mining, to reduce the search space where a genetic algorithm looks for solutions (i.e. gene subsets) with better classification performance, being the quality (fitness) of each solution evaluated by a classification method. The methodology is quite general because any classification algorithm could be incorporated as well a variety of filter methods. Extensive experiments on a public micro-array dataset are presented using four popular filter methods and SVM