Browsing by Issue Date, starting with "2022-05-16"
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- Serine-based surfactants as effective antimicrobial agents against multiresistant bacteriaPublication . Silva, Sandra G.; Pinheiro, Marina; Pereira, Rui; Dias, Ana Rita; Ferraz, Ricardo; Prudêncio, Cristina; Eaton, Peter J.; Reis, Salette; Vale, M. Luísa C. doThe antimicrobial activity of two serine derived gemini cationic surfactants, amide (12Ser)2CON12 and ester (12Ser)2COO12, was tested using sensitive, E. coli ATCC 25922 and S. aureus ATCC 6538, and resistant, E. coli CTX M2, E. coli TEM CTX M9 and S. aureus ATCC 6538 and S. aureus MRSA ATCC 43300 Gram-positive and Gram-negative bacteria strains. Very low MIC values (5 μM) were found for the two resistant strains E.coli TEM CTX M9 and S. aureus MRSA ATCC 43300, in the case of the amide derivative, and for S. aureus MRSA ATCC 43300, in the case of the ester derivative. The interaction of the serine amphiphiles with lipid-model membranes (DPPG and DPPC) was investigated using Langmuir monolayers. A more pronounced effect on the DPPG than on the DPPC monolayer was observed. The effect induced by the surfactants on bacteria membrane was explored by Atomic Force Microscopy. A clear disruption of the bacteria membrane was observed for E. coli TEM CTX M9 upon treatment with (12ser)2CON12, whereas for the S. aureus MRSA few observable changes in cell morphology were found after treatment with either of the two surfactants. The cytotoxicity of the two compounds was assessed by hemolysis assay on human red blood cells (RBC). The compounds were shown to be non-cytotoxic up to 10 μM. Overall, the results reveal a promising potential, in particular of the amide derivative, as antimicrobial agent for two strains of antibiotic resistant bacteria.
- Effects of abdominal microcurrent in the consumption and proportion of energy substrates during aerobic exercise: a pilot studyPublication . Vilarinho, Rui; Faria, Susana Miriam; Monteiro, Pedro; Melo, Cristina; Santos, Rubim; Noites, AndreiaMicrocurrent therapy can increase lipolytic activity. However, it is unknown if the increased availability of lipids can influence the selection of energy substrates during a single session of aerobic exercise. We aimed to analyze the effect of microcurrent application to the abdominal region in the consumption of lipids and carbohydrates, and respiratory exchange ratio (RER) during a single session of moderate aerobic exercise in young adults. A pilot study was conducted in which participants were allocated to intervention (IG) or placebo (PG) groups. In both groups, 40 min of microcurrent application with two frequencies (25 and 10 Hz) followed by 50 min of moderate intensity aerobic exercise (45–55% of heart rate reserve) on a cycloergometer were performed. The microcurrent application was performed without intensity in the PG. A portable gas analyzer (K4b2 ) was used during exercise in both groups. Thirty-eight participants (20.6 ± 1.8 years; 18 in IG and 20 in PG) were enrolled. There were no significant differences in the consumption of substrates or RER between the groups during exercise (p > 0.05). Microcurrent application seems to be insufficient to influence the consumption of energy substrates and RER during a single session of aerobic exercise in young adults.
- Data-driven Deep Reinforcement Learning for Online Flight Resource Allocation in UAVaided Wireless Powered Sensor NetworksPublication . Li, Kai; Ni, Wei; Kurunathan, Harrison; Dressler, FalkoIn wireless powered sensor networks (WPSN), data of ground sensors can be collected or relayed by an unmanned aerial vehicle (UAV) while the battery of the ground sensor can be charged via wireless power transfer. A key challenge of resource allocation in UAV-aided WPSN is to prevent battery drainage and buffer overflow of the ground sensors in the presence of highly dynamic lossy airborne channels which can result in packet reception errors. Moreover, state and action spaces of the resource allocation problem are large, which is hardly explored online. To address the challenges, a new data-driven deep reinforcement learning framework, DDRL-RA, is proposed to train flight resource allocation online so that the data packet loss is minimized. Due to time-varying airborne channels, DDRL-RA firstly leverages long short-term memory (LSTM) with pre-collected offline datasets for channel randomness predictions. Then, Deep Deterministic Policy Gradient (DDPG) is studied to control the flight trajectory of the UAV, and schedule the ground sensor to transmit data and harvest energy. To evaluate the performance of DDRL-RA, a UAV-ground sensor testbed is built, where real-world datasets of channel gains are collected. DDRL-RA is implemented on Tensorflow, and numerical results show that DDRL-RA achieves 19\% lower packet loss than other learning-based frameworks.