A Model for the Biosynthesis and Transport of Plasma Membrane-Associated Signaling Receptors to the Cell Surface

Intracellular protein transport is emerging as critical in determining the outcome of receptor-activated signal transduction pathways. In plants, relatively little is known about the nature of the molecular components and mechanisms involved in coordinating receptor synthesis and transport to the cell surface. Recent advances in this field indicate that signaling pathways and intracellular transport machinery converge and coordinate to render receptors competent for signaling at their plasma membrane (PM) activity sites. The biogenesis and transport to the cell surface of signaling receptors appears to require both general trafficking and receptor-specific factors. Several molecular determinants, residing or associated with compartments of the secretory pathway and known to influence aspects in receptor biogenesis, are discussed and integrated into a predictive cooperative model for the functional expression of signaling receptors at the PM

PCR: A Powerful Method in Food Safety Field

In this chapter, application of the polymerase chain reaction (PCR) technique in food safety, considering all the branches of this concept, is presented. The area of interest contains important analysis for both human health and the identification of food adulteration. PCR techniques used for detection of genetically modified organisms (GMO) in different matrices, identification of different animal species in meat and dairy products, as well as the detection of food infection with food-borne pathogens and toxicogenic fungi are described. The working methods and result analysis are exemplified, starting with DNA isolation adjusted to different matrices, detection of target genes, and validation for all of these methods. Techniques of simplex PCR, primer multiplexing, primer design, validation of the laboratory methods, optimization of the PCR results, and result interpretation through the analysis of the electrophoresis gels and sequencing data are studied. At the same time, the obtained results, the obstacles encountered, and how they were overcome could be an example for specific analysis developed with less resources and also for adapting the existent validated methods to the new laboratory conditions. The practical applicability and the consumer’s demands are of great importance and always must be considered in developing and validating those methods

Plants Root Interference Area, A Benefit To The Microbial Community

Part of byproducts synthesized by plants through photosynthesis reach the ground, where create selective microenvironments for micro-flora and associations of plant - micro-organisms, which are a benefit for plant growth Setting the interference effect of the root interference area of vines and herbaceous plants and of radicular exudates from vine rhizosphere on microbial community and estimating microbial population present on the vine leaves. The biological material was represented by leaves (Fa, Fb), and soil rhizosphere (Ra, Rb) of two varieties of vines (Tamaioasa Romanian white and black / TA, TN), and from the vine roots interference area with other herbaceous plants (Ma, Mb). The soil has never been chemically treated. The microbiological study of biological samples was performed by classical and molecular methods. Overall, bacteria had a significant presence in soil samples taken from the root interference zone (Ma, Mb). Actinomycetes quantitatively dominated the root interference area  of herbaceous plant with variety TA. The range of actinomycetes species and leaves microflora was reduced. In this study we have shown that significant growth of microorganisms occurs in the interference area of vine with other herbal plants as a result of the cumulative effect of radicular exudates

Detecting abnormal events in video using Narrowed Normality Clusters

We formulate the abnormal event detection problem as an outlier detection task and we propose a two-stage algorithm based on k-means clustering and one-class Support Vector Machines (SVM) to eliminate outliers. In the feature extraction stage, we propose to augment spatio-temporal cubes with deep appearance features extracted from the last convolutional layer of a pre-trained neural network. After extracting motion and appearance features from the training video containing only normal events, we apply k-means clustering to find clusters representing different types of normal motion and appearance features. In the first stage, we consider that clusters with fewer samples (with respect to a given threshold) contain mostly outliers, and we eliminate these clusters altogether. In the second stage, we shrink the borders of the remaining clusters by training a one-class SVM model on each cluster. To detected abnormal events in the test video, we analyze each test sample and consider its maximum normality score provided by the trained one-class SVM models, based on the intuition that a test sample can belong to only one cluster of normality. If the test sample does not fit well in any narrowed normality cluster, then it is labeled as abnormal. We compare our method with several state-of-the-art methods on three benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve better results in most cases, while processing the test video in real-time at 24 frames per second on a single CPU.Comment: Accepted at WACV 2019. arXiv admin note: text overlap with arXiv:1705.0818