Perssonmoses4205

In this study, nutraceuticals based on antimicrobial ingredients (Artemisia absinthium water extract and essential oil (EO), Lactobacillus uvarum LUHS245 strain cultivated in a whey media, and blackcurrants juice (BCJ) preparation by-products were developed. In addition, two texture forming agents for nutraceutical preparations were tested (gelatin and agar). The developed nutraceutical ingredients showed antimicrobial properties Artemisia absinthium EO (concentration 0.1%) inhibited methicillin-resistant Staphylococcus aureus, Enterococcus faecium, Bacillus cereus, Streptococcus mutans, Staphylococcus epidermidis, and Pasteurella multocida; LUHS245 strain inhibited 14 from the 15 tested pathogenic strains; and BCP inhibited 13 from the 15 tested pathogenic strains. The best formulation consisted of the Artemisia absinthium EO, LUHS245, and BCP immobilised in agar and this formulation showed higher TPC content (by 2.1% higher), as well as higher overall acceptability (by 17.7% higher), compared with the formulation prepared using gelatin.Driving is a task that puts heavy demands on visual information, thereby the human visual system plays a critical role in making proper decisions for safe driving. Understanding a driver's visual attention and relevant behavior information is a challenging but essential task in advanced driver-assistance systems (ADAS) and efficient autonomous vehicles (AV). Specifically, robust prediction of a driver's attention from images could be a crucial key to assist intelligent vehicle systems where a self-driving car is required to move safely interacting with the surrounding environment. Thus, in this paper, we investigate a human driver's visual behavior in terms of computer vision to estimate the driver's attention locations in images. First, we show that feature representations at high resolution improves visual attention prediction accuracy and localization performance when being fused with features at low-resolution. To demonstrate this, we employ a deep convolutional neural network framework that learns and extracts feature representations at multiple resolutions. In particular, the network maintains the feature representation with the highest resolution at the original image resolution. Second, attention prediction tends to be biased toward centers of images when neural networks are trained using typical visual attention datasets. To avoid overfitting to the center-biased solution, the network is trained using diverse regions of images. Finally, the experimental results verify that our proposed framework improves the prediction accuracy of a driver's attention locations.Hypoxia switches the metabolism of tumor cells and induces drug resistance. Selleck KIF18A-IN-6 Currently, no therapeutic exists that effectively and specifically targets hypoxic cells in tumors. Development of such therapeutics critically depends on the availability of in vitro models that accurately recapitulate hypoxia as found in the tumor microenvironment. Here, we report on the design and validation of an easy-to-fabricate tumor-on-a-chip microfluidic platform that robustly emulates the hypoxic tumor microenvironment. The tumor-on-a-chip model consists of a central chamber for 3D tumor cell culture and two side channels for medium perfusion. The microfluidic device is fabricated from polydimethylsiloxane (PDMS), and oxygen diffusion in the device is blocked by an embedded sheet of polymethyl methacrylate (PMMA). Hypoxia was confirmed using oxygen-sensitive probes and the effect on the 3D tumor cell culture investigated by a pH-sensitive dual-labeled fluorescent dextran and a fluorescently labeled glucose analogue. In contrast to control devices without PMMA, PMMA-containing devices gave rise to decreases in oxygen and pH levels as well as an increased consumption of glucose after two days of culture, indicating a rapid metabolic switch of the tumor cells under hypoxic conditions towards increased glycolysis. This platform will open new avenues for testing anti-cancer therapies targeting hypoxic areas.Fear and anxiety constitute an important theme in dentistry, especially with children. Anxiety and the fear of pain during dental treatment can lead to avoidance behaviour, which contributes to perpetuating fear and anxiety of dental care. Understanding and assessing dental fear and anxiety in children is important for delivering successful dental care with high satisfaction in this age group. Among the vast assessment method options available today, self-report assessment, parental proxy assessment, observation-based assessment, and physiological assessment are the four major types for dental fear and anxiety in children. Each method has its own merits and limitations. The selection of a method should be based on the objectives, validity, and setting of the assessment. The aim of this paper is to review and discuss the assessment methods for dental fear and anxiety in children.Our previous studies have shown that the application of the proposed technique of a dual focused ultrasonic beam in two orthogonal cross-sections in passive (elevation) and active (azimuth) apertures of linear ultrasonic phased array transducer (ULPAT) enhances the 3D spatial resolution in the case of the inspection of conventional defects (flat bottom holes) or measurement of thickness of multi-layered metal composites. The objective of this work is to apply the proposed technique to enhance the spatial resolution of the ULPAT in the cases of detection and sizing demonstration of internal defects possessing spatially complex geometry, and during the inspection of defective multi-layered thin composite components (e.g., GLARE) of the aircraft fuselage. The specially prepared aluminium specimen possessing an internal defect of complicated geometry (crescent-shaped) was investigated. The simulation results and experiments demonstrate the resolution enhancement, higher amplitude of the reflections (e.g., 2.5 times or +8 dB) and spatial improvement in the defect detection even in the case of the non-perpendicular incidence of ultrasonic waves to the complex geometry surface of the internal defect. During the experiments, the multi-layered GFRP-metal based composite sample GLARE 3-3/2 was investigated in the case of the single-side access to the surface of the sample. The internal artificial delamination type defect of 25 mm was detected with a higher accuracy. Compared to the limitations of conventional ULPAT, the relative error (32%) (at the -6 dB level) of lateral defect dimensions estimation was completely reduced.