Hussainhendricks1354

Both quantitative and qualitative evaluations have been made. Outcomes have shown that the method provides a final positioning percentage error of 0.21% on an average distance of 17.2 m. A longer run in an industrial context has provided comparable results (a percentage error of 0.94% after about 80 m). The average relative positioning error is about 3%, which is still in good agreement with current state of the art.To explore Auricularia auricula-judae polysaccharides (AAP) as natural anticoagulants for application in the functional food industry, ultrasound assisted extraction (UAE) was optimized for the extraction of AAP by using a response surface methodology (RSM). The maximum extraction yield of crude AAP (14.74 mg/g) was obtained at the optimized extraction parameters as follows Extraction temperature (74 °C), extraction time (27 min), the ratio of liquid to raw material (103 mL/g), and ultrasound power (198 W). Furthermore, the acidic AAP (aAAP) was precipitated with cetyltrimethylammonium bromide (CTAB) from crude AAP (cAAP). aAAP was further purified using ion exchange chromatography with a DEAE Purose 6 Fast Flow column to obtain aAAP-1. Additionally, according to the HPLC analysis, the aAAP-1 was mainly composed of mannose, glucuronic acid, glucose, galactose, and xylose, with a molar ratio of 80.639.882.25131.13. Moreover, the results of the activated partial thromboplastin time (APTT), prothrombin time (PT), and thrombin time (TT) indicated aAAP-1 had anticoagulant activity, which was a synergic anticoagulant activity by the endogenous and exogenous pathway.Legionella pneumophila is a facultative intracellular pathogen found in aquatic environments as planktonic cells within biofilms and as intracellular parasites of free-living amoebae such as Acanthamoeba castellanii. This pathogen bypasses the elimination mechanism to replicate within amoebae; however, not all amoeba species support the growth of L. pneumophila. Willaertia magna C2c Maky, a non-pathogenic amoeba, was previously demonstrated to possess the ability to eliminate the L. pneumophila strain Paris. Here, we study the intracellular behaviour of three L. pneumophila strains (Paris, Philadelphia, and Lens) within W. magna C2c Maky and compare this strain to A. castellanii and W. magna Z503, which are used as controls. We observe the intracellular growth of strain Lens within W. magna Z503 and A. castellanii at 22 °C and 37 °C. Strain Paris grows within A. castellanii at any temperature, while it only grows at 22 °C within W. magna Z503. Strain Philadelphia proliferates only within A. castellanii at 37 °C. Within W. magna C2c Maky, none of the three legionella strains exhibit intracellular growth. Additionally, the ability of W. magna C2c Maky to decrease the number of internalized L. pneumophila is confirmed. this website These results support the idea that W. magna C2c Maky possesses unique behaviour in regard to L. pneumophila strains.Biodegradation is one of the most effective and profitable methods for the elimination of toxic polychlorinated biphenyls (PCBs) and total petroleum hydrocarbons (TPH) from the environment. In this study, aerobic degradation of the mentioned pollutants by bacterial strains Mycolicibacterium frederiksbergense IN53, Rhodococcus erythropolis IN129, and Rhodococcus sp. IN306 and mixed culture M1 developed based on those strains at 111 ratio was analyzed. The effectiveness of individual strains and of the mixed culture was assessed based on carried out respirometric tests and chromatographic analyses. The Rhodococcus sp. IN306 turned out most effective in terms of 18 PCB congeners biodegradation (54.4%). The biodegradation index was decreasing with an increasing number of chlorine atoms in a molecule. Instead, the Mycolicobacterium frederiksbergense IN53 was the best TPH degrader (37.2%). In a sterile soil, contaminated with PCBs and TPH, the highest biodegradation effectiveness was obtained using inoculation with mixed culture M1, which allowed to reduce both the PCBs (51.8%) and TPH (34.6%) content. The PCBs and TPH biodegradation capacity of the defined mixed culture M1 was verified ex-situ with prism method in a non-sterile soil polluted with aged petroleum hydrocarbons (TPH) and spent transformer oil (PCBs). After inoculation with mixed culture M1, the PCBs were reduced during 6 months by 84.5% and TPH by 70.8% as well as soil toxicity was decreased.Quantitative analysis of endocrine-disrupting molecules such as bisphenol A (BPA) in freshwater to determine their widespread occurrence in environmental resources has been challenged by various adsorption and desorption processes. In this work, ion trap mass spectrometry (ITMS) analysis of BPA was aimed at studying its molecular interactions with titanium dioxide (TiO2) nanoparticles and milk whey proteins. Addition of sodium formate prevented TiO2 nanoparticles from sedimentation while enhancing the electrospray ionization (ESI) efficiency to produce an abundance of [BPA + Na]+ ions at m/z 251.0. More importantly, the ESI-ITMS instrument could operate properly during a direct infusion of nanoparticles up to 500 μg/mL without clogging the intake capillary. Milk protein adsorption of BPA could decrease the [BPA + Na]+ peak intensity significantly unless the proteins were partially removed by curdling to produce whey, which allowed BPA desorption during ESI for quantitative analysis by ITMS.Near-infrared (NIR) spectral sensors can deliver the spectral response of light absorbed by materials. Data analysis technology based on NIR sensors has been a useful tool for quality identification. In this paper, an improved deep convolutional neural network (CNN) with batch normalization and MSRA (Microsoft Research Asia) initialization is proposed to discriminate the tobacco cultivation regions using data collected from NIR sensors. The network structure is created with six convolutional layers and three full connection layers, and the learning rate is controlled by exponential attenuation method. One-dimensional kernel is applied as the convolution kernel to extract features. Meanwhile, the methods of L2 regularization and dropout are used to avoid the overfitting problem, which improve the generalization ability of the network. Experimental results show that the proposed deep network structure can effectively extract the complex characteristics inside the spectrum, which proves that it has excellent recognition performance on tobacco cultivation region discrimination, and it also demonstrates that the deep CNN is more suitable for information mining and analysis of big data.