Inhibitory Effects of Several Essential Oils towards Salmonella typhimurium, Salmonella paratyphi A and Salmonella paratyphi B

Plant essential oils are natural products extracted from plants and because of their antimicrobial properties can be used as natural additives in foods. They are also useful for decontamination of food-borne pathogens and can be a safe additive in foods. The antimicrobial activities of essential oils belonging to Saturiea hortensis, Thymus vulgaris, Mentha polegium, Cuminum cyminum, Lavandula officinalis and Mentha viridis L. (spearmint) were investigated at different concentrations (0.1, 0.3, 0.5, 1, 2, 5 and 10%v/v) against Salmonella typhimurium, Salmonella paratyphi A and Salmonella paratyphi B by using the agar well diffusion method. Essential oils showed inhibitory effect on Salmonella spp. in the agar well diffusion assay. In addition, the capability of essential oils for decontamination of minced row beef, ground beef, minced raw chicken and minced raw fish inoculated with Salmonella spp. at 0.1 and 0.5%v/v were assessed. Reduction of the Salmonella spp. population was observed following the inoculation of the cultures with 0.1 and 0.5%v/v essential oils

The Stability of Gauss Model Having One-Prey and Two-Predators

The study of the dynamics of predator-prey interactions can be recognized as a major issue in mathematical biology. In the present paper, some Gauss predator-prey models in which three ecologically interacting species have been considered and the behavior of their solutions in the stability aspect have been investigated. The main aim of this paper is to consider the local and global stability properties of the equilibrium points for represented systems. Finally, stability of some examples of Gauss model with one prey and two predators is discussed

A surrogate model based on a finite element model of abdomen for real-time visualisation of tissue stress during physical examination training

Robotic patients show great potential to improve medical palpation training as they can provide feedback that cannot be obtained in a real patient. Providing information about internal organs deformation can significantly enhance palpation training by giving medical trainees visual insight based on their finger behaviours. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, thus able to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real-time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANN) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 hrs to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that the ANN has a 92.6% accuracy. We also show that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has potential to be used as a training simulator for trainees to hone their palpation skills

Authoritative subspecies diagnosis tool for European honey bees based on ancestryinformative SNPs

Background With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and F-ST) to select the most informative SNPs for ancestry inference. Results Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% +/- 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.The SmartBees project was funded by the European Commission under its FP7 KBBE programme (2013.1.3-02, SmartBees Grant Agreement number 613960) https://ec.europa.eu/research/fp7.MP was supported by a Basque Government grant (IT1233-19). The funders provided the financial support to the research, but had no role in the design of the study, analysis, interpretations of data and in writing the manuscript

Approaches in biotechnological applications of natural polymers

Natural polymers, such as gums and mucilage, are biocompatible, cheap, easily available and non-toxic materials of native origin. These polymers are increasingly preferred over synthetic materials for industrial applications due to their intrinsic properties, as well as they are considered alternative sources of raw materials since they present characteristics of sustainability, biodegradability and biosafety. As definition, gums and mucilages are polysaccharides or complex carbohydrates consisting of one or more monosaccharides or their derivatives linked in bewildering variety of linkages and structures. Natural gums are considered polysaccharides naturally occurring in varieties of plant seeds and exudates, tree or shrub exudates, seaweed extracts, fungi, bacteria, and animal sources. Water-soluble gums, also known as hydrocolloids, are considered exudates and are pathological products; therefore, they do not form a part of cell wall. On the other hand, mucilages are part of cell and physiological products. It is important to highlight that gums represent the largest amounts of polymer materials derived from plants. Gums have enormously large and broad applications in both food and non-food industries, being commonly used as thickening, binding, emulsifying, suspending, stabilizing agents and matrices for drug release in pharmaceutical and cosmetic industries. In the food industry, their gelling properties and the ability to mold edible films and coatings are extensively studied. The use of gums depends on the intrinsic properties that they provide, often at costs below those of synthetic polymers. For upgrading the value of gums, they are being processed into various forms, including the most recent nanomaterials, for various biotechnological applications. Thus, the main natural polymers including galactomannans, cellulose, chitin, agar, carrageenan, alginate, cashew gum, pectin and starch, in addition to the current researches about them are reviewed in this article.. }To the Conselho Nacional de Desenvolvimento Cientfíico e Tecnológico (CNPq) for fellowships (LCBBC and MGCC) and the Coordenação de Aperfeiçoamento de Pessoal de Nvíel Superior (CAPES) (PBSA). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and COMPETE 2020 (POCI-01-0145-FEDER-006684) (JAT)