ALBERT

Description: This manuscript deals with the synthesis and computational and experimental evaluation of the antimicrobial activity of twenty-nine 4-(indol-3-yl) thiazole-2-amines and 4-ιndol-3-yl)thiazole acylamines. An evaluation of antibacterial activity against Gram (+) and Gram (−) bacteria revealed that the MIC of indole derivatives is in the range of 0.06–1.88 mg/mL, while among fourteen methylindole derivatives, only six were active, with an MIC in the range of of 0.47–1.88 mg/mL. S. aureus appeared to be the most resistant strain, while S. Typhimurium was the most sensitive. Compound 5x was the most promising, with an MIC in the range of 0.06–0.12 mg/mL, followed by 5d and 5m. An evaluation of these three compounds against resistant strains, namely MRSA P. aeruginosa and E. coli, revealed that they were more potent against MRSA than ampicillin. Furthermore, compounds 5m and 5x were superior inhibitors of biofilm formation, compared to ampicillin and streptomycin, in terms Compounds 5d, 5m, and 5x interact with streptomycin in additive manner. The antifungal activity of some compounds exceeded or was equipotent to those of the reference antifungal agents bifonazole and ketoconazole. The most potent antifungal agent was found to be compound 5g. Drug likeness scores of compounds was in a range of −0.63 to 0.29, which is moderate to good. According to docking studies, E. coli MurB inhibition is probably responsible for the antibacterial activity of compounds, whereas CYP51 inhibition was implicated in antifungal activity. Compounds appeared to be non-toxic, according to the cytotoxicity assessment in MRC-5 cells.

Description: The impact of plum pox virus (PPV) on sour cherry (Prunus cerasus L.) productivity has been studied by comparing the yield of PPV-infected and PPV-free fruit-bearing trees. A total of 152 16- to 17-year-old trees of nine cultivars and hybrids were surveyed in the production orchards (cultivar collection and hybrid testing plots) in the Republic of Tatarstan, Russia. Sixty trees tested positive for PPV using ELISA and RT-PCR. Among them, 58 PPV isolates belonged to the strain C and the other 2 isolates to the strain CV. For the cultivars Sevastyanovskaya, Shakirovskaya, hybrids 88-2 and 80-8, the average (2012 to 2019) productivity of infected trees was 38% to 45% lower than for PPV-free trees of the same cultivar or hybrid. No ilarviruses (prunus necrotic ringspot virus, prune dwarf virus, apple mosaic virus, American plum line pattern virus) were detected in PPV-infected trees, suggesting that reduced cherry productivity was attributed to the PPV infection. Thus, it was shown for the first time that PPV can reduce the productivity of at least some sour cherry cultivars and hybrids, and strain C isolates are responsible for crop losses.

Description: With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long–Short Term Memory (BiLSTM) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which were used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.