Synthesis, characterization and antimicrobial activities of some 5-bromouracilmetal ion complexes

Six new complexes, [Mn(Br-U)2(H2O)2]×4H2O (1), [Cd(Br-U)2]×2H2O (2), [Cu(Br-U)2(H2O)2]×2H2O (3), [Co(Br-U)2(H2O)2]×4H2O (4), [Ni(Br-U)2(H2O)2]×4H2O (5) and [Ag(Br-U)(Br-U-H)]×2(H2O) (6)  were prepared by the reaction of 5-bromoouracil with MnCl2·4H2O, CdCl2·2.5H2O, CuSO4·5H2O, (CH3COO)2Co·4H2O, (CH3COO)2Ni·4H2O and AgNO3 respectively. The complexes were characterized by melting point, elemental microanalyses, IR and 1H NMR spectroscopy. The obtained data indicated that the ligand interacted with the metal ions in its mononegatively charged enol form in a bidentate fashion. Thermogravimetric analyses (TGA and DTG) were also carried out. The data obtained agreed well the proposed structures and showed that the complexes were finally decomposed to the corresponding metal or metal oxide. The ligand and its metal-ion complexes were tested for their antimicrobial activities against four bacterial strains (B. subtillis, S. aureus, E. coli and P. aeruginosa) by the agar-well diffusion technique using DMSO as a solvent. The obtained data showed that the complexes were more potent antimicrobial agents than the parent ligand.               KEY WORDS: 5-Bromoouracil–M2+ complexes, IR, Thermal analyses, 1H NMR, Antimicrobial activity Bull. Chem. Soc. Ethiop. 2019, 33(2), 255-268.DOI: https://dx.doi.org/10.4314/bcse.v33i2.

Planetary gearbox condition monitoring based on modulation analysis

The epicycle gearbox or planetary gearbox (PG) is a central power transmission systems of important machines such as helicopters and wind turbines which are mission critical and high cost systems. Condition monitoring (CM) has been explored extensively in recent years to avoid any unexpected interruptions and severe accidences caused by faults PGs. Although, considerable advancements in CM techniques, there still existed significant deficiency such as insensitivity, false diagnosis and high costs in implementing such techniques in industries. To improve CM techniques, therefore, this thesis focuses on an investigation of advanced signal analysis techniques such as higher order spectra (HOS) in order to achieve full characterisation of the nonlinear modulation processes of PG dynamics and thereby develop accurate diagnostic techniques. The lumped mass model is established for modelling the dynamic behaviour of the PG under investigation, which allows the vibration behaviours to be understood for analysing different abnormalities such as tooth breakages and gear errors. This paves the way for subsequent data analytics and fault diagnostics using modulation signal bispectrum (MSB) that allows the vibration data to be examined through HOS, but it is significantly efficient in characterising the multiple and nonlinear modulations of PG dynamics alongside superior noise reduction performance. Different degrees of misalignments in the PG drive system has been investigated and successfully diagnosed using MSB analysis of vibration measurements.. Moreover, the investigation included detection of tooth breakage faults of different severities in both the sun and a planet gear. The tooth faults were diagnosed using the recently developed MSB through accurately representation and estimate of residual sidebands induced by these faults. Consequently, MSB analysis produces an accurate and reliable diagnosis in that it gives correct indication of the fault severity and location for wide operating conditions. Furthermore, these fault diagnosis practices allows the establishment of residual sideband analysis approach. These residual sidebands resulting from the out-of-phase superposition of vibration waves due to asymmetric, multiple meshing sources are much less influenced by gear errors than the in-phase sidebands due to faults or new occurrences of the symmetricity. MSB can provide an accurate characterisation of the residual sidebands and consequently produces consistent diagnosis as confirmed by both simulation and experiment

A Study on the Impact of Integrating Reinforcement Learning for Channel Prediction and Power Allocation Scheme in MISO-NOMA System

Data Availability Statement: Not applicable.Copyright © 2023 by the authors. In this study, the influence of adopting Reinforcement Learning (RL) to predict the channel parameters for user devices in a Power Domain Multi-Input Single-Output Non-Orthogonal Multiple Access (MISO-NOMA) system is inspected. In the channel prediction-based RL approach, the Q-learning algorithm is developed and incorporated into the NOMA system so that the developed Q-model can be employed to predict the channel coefficients for every user device. The purpose of adopting the developed Q-learning procedure is to maximize the received downlink sum-rate and decrease the estimation loss. To satisfy this aim, the developed Q-algorithm is initialized using different channel statistics and then the algorithm is updated based on the interaction with the environment in order to approximate the channel coefficients for each device. The predicted parameters are utilized at the receiver side to recover the desired data. Furthermore, based on maximizing the sum-rate of the examined user devices, the power factors for each user can be deduced analytically to allocate the optimal power factor for every user device in the system. In addition, this work inspects how the channel prediction based on the developed Q-learning model, and the power allocation policy, can both be incorporated for the purpose of multiuser recognition in the examined MISO-NOMA system. Simulation results, based on several performance metrics, have demonstrated that the developed Q-learning algorithm can be a competitive algorithm for channel estimation when compared to different benchmark schemes such as deep learning-based long short-term memory (LSTM), RL based actor-critic algorithm, RL based state-action-reward-state-action (SARSA) algorithm, and standard channel estimation scheme based on minimum mean square error procedure.This research received no external funding

Investigating the Combination of Deep Learning for Channel Estimation and Power Optimization in a Non-Orthogonal Multiple Access System

Data Availability Statement: Not applicable.Copyright: © 2022 by the authors. In a non-orthogonal multiple access (NOMA) system, the successive interference cancellation (SIC) procedure is typically employed at the receiver side, where several user’s signals are decoded in a subsequent manner. Fading channels may disperse the transmitted signal and originate dependencies among its samples, which may affect the channel estimation procedure and consequently affect the SIC process and signal detection accuracy. In this work, the impact of Deep Neural Network (DNN) in explicitly estimating the channel coefficients for each user in NOMA cell is investigated in both Rayleigh and Rician fading channels. The proposed approach integrates the Long Short-Term Memory (LSTM) network into the NOMA system where this LSTM network is utilized to predict the channel coefficients. DNN is trained using different channel statistics and then utilized to predict the desired channel parameters that will be exploited by the receiver to retrieve the original data. Furthermore, this work examines how the channel estimation based on Deep Learning (DL) and power optimization scheme are jointly utilized for multiuser (MU) recognition in downlink Power Domain Non-Orthogonal Multiple Access (PD-NOMA) system. Power factors are optimized with a view to maximize the sum rate of the users on the basis of entire power transmitted and Quality of service (QoS) constraints. An investigation for the optimization problem is given where Lagrange function and Karush–Kuhn–Tucker (KKT) optimality conditions are applied to deduce the optimum power coefficients. Simulation results for different metrics, such as bit error rate (BER), sum rate, outage probability and individual user capacity, have proved the superiority of the proposed DL-based channel estimation over conventional NOMA approach. Additionally, the performance of optimized power scheme and fixed power scheme are evaluated when DL-based channel estimation is implemented.Funding: This research received no external funding

Multi-parametric arterial spin labelling and diffusion-weighted magnetic resonance imaging in differentiation of grade II and grade III gliomas

Purpose: To assess arterial spin labelling (ASL) perfusion and diffusion MR imaging (DWI) in the differentiation of grade II from grade III gliomas. Material and methods: A prospective cohort study was done on 36 patients (20 male and 16 female) with diffuse gliomas, who underwent ASL and DWI. Diffuse gliomas were classified into grade II and grade III. Calculation of tumoural blood flow (TBF) and apparent diffusion coefficient (ADC) of the tumoral and peritumoural regions was made. The ROC curve was drawn to differentiate grade II from grade III gliomas. Results: There was a significant difference in TBF of tumoural and peritumoural regions of grade II and III gliomas (p = 0.02 and p =0.001, respectively). Selection of 26.1 and 14.8 ml/100 g/min as the cut-off for TBF of tumoural and peritumoural regions differentiated between both groups with area under curve (AUC) of 0.69 and 0.957, and accuracy of 77.8% and 88.9%, respectively. There was small but significant difference in the ADC of tumoural and peritumoural regions between grade II and III gliomas (p = 0.02 for both). The selection of 1.06 and 1.36 × 10-3 mm2/s as the cut-off of ADC of tumoural and peritumoural regions was made, to differentiate grade II from III with AUC of 0.701 and 0.748, and accuracy of 80.6% and 80.6%, respectively. Combined TBF and ADC of tumoural regions revealed an AUC of 0.808 and accuracy of 72.7%. Combined TBF and ADC for peritumoural regions revealed an AUC of 0.96 and accuracy of 94.4%. Conclusion: TBF and ADC of tumoural and peritumoural regions are accurate non-invasive methods of differentiation of grade II from grade III gliomas

High-fat, sucrose and salt-rich diet during rat spermatogenesis lead to the development of chronic kidney disease in the female offspring of the F2 generation

Effects of feeding male rats during spermatogenesis a high-fat, high-sucrose and high-salt diet (HFSSD) over two generations (F0 and F1) on renal outcomes are unknown. Male F0 and F1 rats were fed either control diet (F0CD+F1CD) or HFSSD (F0HD+F1HD). The outcomes were glomerular filtration rate and urinary albumin excretion in F1 and F2 offspring. If both outcomes were altered a morphological and molecular assessment was done. F2 offspring of both sexes had a decreased GFR. However, increased urinary albumin excretion was only observed in female F2 F0HD+F1HD offspring compared with controls. F0HD+F1HD female F2 offspring developed glomerulosclerosis (+31%; p < .01) and increased renal interstitial fibrosis (+52%; p < .05). RNA sequencing followed by qRT-PCR validation showed that four genes (Enpp6, Tmem144, Cd300lf, and Actr3b) were differentially regulated in the kidneys of female F2 offspring. lncRNA XR-146683.1 expression decreased in female F0HD+F1HD F2 offspring and its expression was (r = 0.44, p = .027) correlated with the expression of Tmem144. Methylation of CpG islands in the promoter region of the Cd300lf gene was increased (p = .001) in female F2 F0HD+F1HD offspring compared to controls. Promoter CpG island methylation rate of Cd300lf was inversely correlated with Cd300lf mRNA expression in F2 female offspring (r = −0.483, p = .012). Cd300lf mRNA expression was inversely correlated with the urinary albumin-to-creatinine ratio in female F2 offspring (r = −0.588, p = .005). Paternal pre-conceptional unhealthy diet given for two generations predispose female F2 offspring to chronic kidney disease due to epigenetic alterations of renal gene expression. Particularly, Cd300lf gene promotor methylation was inversely associated with Cd300lf mRNA expression and Cd300lf mRNA expression itself was inversely associated with urinary albumin excretion in F2 female offspring whose fathers and grandfathers got a pre-conceptional unhealthy diet

The tetraspanin CD53 protects stressed hematopoietic stem cells via promotion of DREAM complex-mediated quiescence

The hematopoietic stem cell (HSC) cycle responds to inflammatory and other proliferative stressors; however, these cells must quickly return to quiescence to avoid exhaustion and maintain their functional integrity. The mechanisms that regulate this return to quiescence are not well understood. Here, we show that tetraspanin CD53 is markedly upregulated in HSCs in response to a variety of inflammatory and proliferative stimuli and that the loss of CD53 is associated with prolonged cycling and reduced HSC function in the context of inflammatory stress. Mechanistically, CD53 promotes the activity of the dimerization partner, RB-like, E2F, and multi-vulva class B (DREAM) transcriptional repressor complex, which downregulates genes associated with cycling and division. Proximity labeling and confocal fluorescence microscopy studies showed that CD53 interacts with DREAM-associated proteins, specifically promoting the interaction between Rbl2/p130 and its phosphatase protein phosphatase 2A (PP2A), effectively stabilizing p130 protein availability for DREAM binding. Together, these data identified a novel mechanism by which stressed HSCs resist cycling